Earthquakeearlywarning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an Mw (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the Mw 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing. PMID:26601167

Earthquakeearlywarning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Earthquakeearlywarning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an Mw (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the Mw 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Although earthquakeearlywarning (EEW) has shown great promise for reducing loss of life and property, it has only been implemented in a few regions due, in part, to the prohibitive cost of building the required dense seismic and geodetic networks. However, many cars and consumer smartphones, tablets, laptops, and similar devices contain low-cost versions of the same sensors used for earthquake monitoring. If a workable EEW system could be implemented based on either crowd-sourced observations from consumer devices or very inexpensive networks of instruments built from consumer-quality sensors, EEW coverage could potentially be expanded worldwide. Controlled tests of several accelerometers and global navigation satellite system (GNSS) receivers typically found in consumer devices show that, while they are significantly noisier than scientific-grade instruments, they are still accurate enough to capture displacements from moderate and large magnitude earthquakes. The accuracy of these sensors varies greatly depending on the type of data collected. Raw coarse acquisition (C/A) code GPS data are relatively noisy. These observations have a surface displacement detection threshold approaching ~1 m and would thus only be useful in large Mw 8+ earthquakes. However, incorporating either satellite-based differential corrections or using a Kalman filter to combine the raw GNSS data with low-cost acceleration data (such as from a smartphone) decreases the noise dramatically. These approaches allow detection thresholds as low as 5 cm, potentially enabling accurate warnings for earthquakes as small as Mw 6.5. Simulated performance tests show that, with data contributed from only a very small fraction of the population, a crowd-sourced EEW system would be capable of warning San Francisco and San Jose of a Mw 7 rupture on California's Hayward fault and could have accurately issued both earthquake and tsunami warnings for the 2011 Mw 9 Tohoku-oki, Japan earthquake.

Development of an earthquakeearlywarning capability and pilot project were objectives of TriNet, a 5-year (1997-2001) FEMA-funded project to develop a state-of-the-art digital seismic network in southern California. In parallel with research to assemble a protocol for rapid analysis of earthquake data and transmission of a signal by TriNet scientists and engineers, the public policy, communication and educational issues inherent in implementation of an earthquakeearlywarning system were addressed by TriNet's outreach component. These studies included: 1) a survey that identified potential users of an earthquakeearlywarning system and how an earthquakeearlywarning might be used in responding to an event, 2) a review of warning systems and communication issues associated with other natural hazards and how lessons learned might be applied to an alerting system for earthquakes, 3) an analysis of organization, management and public policy issues that must be addressed if a broad-based warning system is to be developed and 4) a plan to provide earthquakeearlywarnings to a small number of organizations in southern California as an experimental prototype. These studies provided needed insights into the social and cultural environment in which this new technology will be introduced, an environment with opportunities to enhance our response capabilities but also an environment with significant barriers to overcome to achieve a system that can be sustained and supported. In this presentation we will address the main public policy issues that were subjects of analysis in these studies. They include a discussion of the possible division of functions among organizations likely to be the principle partners in the management of an earthquakeearlywarning system. Drawing on lessons learned from warning systems for other hazards, we will review the potential impacts of false alarms and missed events on warning system credibility, the acceptability of fully automated

Rapid assessment of damage potential and size of an earthquake at the station is highly demanded for onsite earthquakeearlywarning. We study the application of τc*Pd for its estimation on the earthquake size using 123 events recorded by the borehole stations of KiK-net in Japan. The new type of earthquake size determined by τc*Pd is more related to the damage potential. We find that τc*Pd provides another parameter to measure the size of earthquake and the threshold to warn strong ground motion.

The U.S. Geological Survey (USGS) and partners are transitioning from test-user trials of a demonstration earthquakeearlywarning system (ShakeAlert) to deciding and preparing how to implement the release of earthquakeearlywarning information, alert messages, and products to the public and other stakeholders. An earthquakeearlywarning system uses seismic station networks to rapidly gather information about an occurring earthquake and send notifications to user devices ahead of the arrival of potentially damaging ground shaking at their locations. Earthquakeearlywarning alerts can thereby allow time for actions to protect lives and property before arrival of damaging shaking, if users are properly educated on how to use and react to such notifications. A collaboration team of risk communications researchers and earth scientists is researching the effectiveness of a chosen subset of potential earthquakeearlywarning interface designs and messages, which could be displayed on a device such as a smartphone. Preliminary results indicate, for instance, that users prefer alerts that include 1) a map to relate their location to the earthquake and 2) instructions for what to do in response to the expected level of shaking. A number of important factors must be considered to design a message that will promote appropriate self-protective behavior. While users prefer to see a map, how much information can be processed in limited time? Are graphical representations of wavefronts helpful or confusing? The most important factor to promote a helpful response is the predicted earthquake intensity, or how strong the expected shaking will be at the user's location. Unlike Japanese users of earlywarning, few Californians are familiar with the earthquake intensity scale, so we are exploring how differentiating instructions between intensity levels (e.g., "Be aware" for lower shaking levels and "Drop, cover, hold on" at high levels) can be paired with self-directed supplemental

The development of earthquakeearlywarning capabilities in the United States is now accelerating and expanding as the technical capability to provide warning is demonstrated and additional funding resources are making it possible to expand the current testing region to the entire west coast (California, Oregon and Washington). Over the course of the next two years we plan to build a prototype system that will provide a blueprint for a full public system in the US. California currently has a demonstrations warning system, ShakeAlert, that provides alerts to a group of test users from the public and private sector. These include biotech companies, technology companies, the entertainment industry, the transportation sector, and the emergency planning and response community. Most groups are currently in an evaluation mode, receiving the alerts and developing protocols for future response. The Bay Area Rapid Transit (BART) system is the one group who has now implemented an automated response to the warning system. BART now stops trains when an earthquake of sufficient size is detected. Research and development also continues to develop improved earlywarning algorithms to better predict the distribution of shaking in large earthquakes when the finiteness of the source becomes important. The algorithms under development include the use of both seismic and GPS instrumentation and integration with existing point source algorithms. At the same time, initial testing and development of algorithms in and for the Pacific Northwest is underway. In this presentation we will review the current status of the systems, highlight the new research developments, and lay out a pathway to a full public system for the US west coast. The research and development described is ongoing at Caltech, UC Berkeley, University of Washington, ETH Zurich, Southern California Earthquake Center, and the US Geological Survey, and is funded by the Gordon and Betty Moore Foundation and the US Geological

Performance of earthquakeearlywarning systems suffers from false alerts caused by local impulsive noise from natural or anthropogenic sources. To mitigate this problem, we train a generative adversarial network (GAN) to learn the characteristics of first-arrival earthquake P waves, using 300,000 waveforms recorded in southern California and Japan. We apply the GAN critic as an automatic feature extractor and train a Random Forest classifier with about 700,000 earthquake and noise waveforms. We show that the discriminator can recognize 99.2% of the earthquake P waves and 98.4% of the noise signals. This state-of-the-art performance is expected to reduce significantly the number of false triggers from local impulsive noise. Our study demonstrates that GANs can discover a compact and effective representation of seismic waves, which has the potential for wide applications in seismology.

EWS for Vrancea earthquakes uses the time interval (28-32 sec.) between the moment when the earthquake is detected by the local seismic network installed in the epicenter area (Vrancea) and the arrival time of the seismic waves in the protected area (Bucharest) to send earthquakewarning to users. In the last years, National Institute for Earth Physics (NIEP) upgraded its seismic network in order to cover better the seismic zones of Romania. Currently the National Institute for Earth Physics (NIEP) operates a real-time seismic network designed to monitor the seismic activity on the Romania territory, dominated by the Vrancea intermediate-depth (60-200 km) earthquakes. The NIEP real-time network consists of 102 stations and two seismic arrays equipped with different high quality digitizers (Kinemetrics K2, Quanterra Q330, Quanterra Q330HR, PS6-26, Basalt), broadband and short period seismometers (CMG3ESP, CMG40T, KS2000, KS54000, KS2000, CMG3T,STS2, SH-1, S13, Ranger, gs21, Mark l22) and acceleration sensors (Episensor). Recent improvement of the seismic network and real-time communication technologies allows implementation of a nation-wide EEWS for Vrancea and other seismic sources from Romania. We present a regional approach to EarthquakeEarlyWarning for Romania earthquakes. The regional approach is based on PRESTo (Probabilistic and Evolutionary earlywarning SysTem) software platform: PRESTo processes in real-time three channel acceleration data streams: once the P-waves arrival have been detected, it provides earthquake location and magnitude estimations, and peak ground motion predictions at target sites. PRESTo is currently implemented in real- time at National Institute for Earth Physics, Bucharest for several months in parallel with a secondary EEWS. The alert notification is issued only when both systems validate each other. Here we present the results obtained using offline earthquakes originating from Vrancea area together with several real

EarthquakeEarlyWarning (EEW) is a system that can provide a few to tens of seconds warning prior to ground shaking at a user's location. The goal and purpose of such a system is to reduce, or minimize, the damage, costs, and casualties resulting from an earthquake. A demonstration earthquakeearlywarning system (ShakeAlert) is undergoing testing in the United States by the UC Berkeley Seismological Laboratory, Caltech, ETH Zurich, University of Washington, the USGS, and beta users in California and the Pacific Northwest. The beta users receive earthquake information very rapidly in real-time and are providing feedback on their experiences of performance and potential uses within their organization. Beta user interactions allow the ShakeAlert team to discern: which alert delivery options are most effective, what changes would make the UserDisplay more useful in a pre-disaster situation, and most importantly, what actions users plan to take for various scenarios. Actions could include: personal safety approaches, such as drop cover, and hold on; automated processes and procedures, such as opening elevator or fire stations doors; or situational awareness. Users are beginning to determine which policy and technological changes may need to be enacted, and funding requirements to implement their automated controls. The use of models and mobile apps are beginning to augment the basic Java desktop applet. Modeling allows beta users to test their earlywarning responses against various scenarios without having to wait for a real event. Mobile apps are also changing the possible response landscape, providing other avenues for people to receive information. All of these combine to improve business continuity and resiliency.

The mega-thrust, Mw 9.0, 2011 Tohoku earthquake has re-opened the discussion among the scientific community about the effectiveness of EarthquakeEarlyWarning (EEW) systems, when applied to such large events. Many EEW systems are now under-testing or -development worldwide and most of them are based on the real-time measurement of ground motion parameters in a few second window after the P-wave arrival. Currently, we are using the initial Peak Displacement (Pd), and the Predominant Period (τc), among other parameters, to rapidly estimate the earthquake magnitude and damage potential. A well known problem about the real-time estimation of the magnitude is the parameter saturation. Several authors have shown that the scaling laws between earlywarning parameters and magnitude are robust and effective up to magnitude 6.5-7; the correlation, however, has not yet been verified for larger events. The Tohoku earthquake occurred near the East coast of Honshu, Japan, on the subduction boundary between the Pacific and the Okhotsk plates. The high quality Kik- and K- networks provided a large quantity of strong motion records of the mainshock, with a wide azimuthal coverage both along the Japan coast and inland. More than 300 3-component accelerograms have been available, with an epicentral distance ranging from about 100 km up to more than 500 km. This earthquake thus presents an optimal case study for testing the physical bases of earlywarning and to investigate the feasibility of a real-time estimation of earthquake size and damage potential even for M > 7 earthquakes. In the present work we used the acceleration waveform data of the main shock for stations along the coast, up to 200 km epicentral distance. We measured the earlywarning parameters, Pd and τc, within different time windows, starting from 3 seconds, and expanding the testing time window up to 30 seconds. The aim is to verify the correlation of these parameters with Peak Ground Velocity and Magnitude

As part of the preparations for the future earthquake in Istanbul a Rapid Response and EarlyWarning system in the metropolitan area is in operation. For the EarlyWarning system ten strong motion stations were installed as close as possible to the fault zone. Continuous on-line data from these stations via digital radio modem provide earlywarning for potentially disastrous earthquakes. Considering the complexity of fault rupture and the short fault distances involved, a simple and robust EarlyWarning algorithm, based on the exceedance of specified threshold time domain amplitude levels is implemented. The band-pass filtered accelerations and the cumulative absolute velocity (CAV) are compared with specified threshold levels. When any acceleration or CAV (on any channel) in a given station exceeds specific threshold values it is considered a vote. Whenever we have 2 station votes within selectable time interval, after the first vote, the first alarm is declared. In order to specify the appropriate threshold levels a data set of near field strong ground motions records form Turkey and the world has been analyzed. Correlations among these thresholds in terms of the epicenter distance the magnitude of the earthquake have been studied. The encrypted earlywarning signals will be communicated to the respective end users by UHF systems through a "service provider" company. The users of the earlywarning signal will be power and gas companies, nuclear research facilities, critical chemical factories, subway system and several high-rise buildings. Depending on the location of the earthquake (initiation of fault rupture) and the recipient facility the alarm time can be as high as about 8s. For the rapid response system one hundred 18 bit-resolution strong motion accelerometers were placed in quasi-free field locations (basement of small buildings) in the populated areas of the city, within an area of approximately 50x30km, to constitute a network that will enable early

Japan Meteorological Agency(JMA) started to provide EarthquakeEarlyWarning(EEW) to the general public in October 2007. It was followed by provision of EEW to a limited number of users who understand the technical limit of EEW and can utilize it for automatic control from August 2006. EarthquakeEarlyWarning in Japan definitely means information of estimated amplitude and arrival time of a strong ground motion after fault rupture occurred. In other words, the EEW provided by JMA is defined as a forecast of a strong ground motion before the strong motion arrival. EEW of JMA is to enable advance countermeasures to disasters caused by strong ground motions with providing a warning message of anticipating strong ground motion before the S wave arrival. However, due to its very short available time period, there should need some measures and ideas to provide rapidly EEW and utilize it properly. - EEW is issued to general public when the maximum seismic intensity 5 lower (JMA scale) or greater is expected. - EEW message contains origin time, epicentral region name, and names of areas (unit is about 1/3 to 1/4 of one prefecture) where seismic intensity 4 or greater is expected. Expected arrival time is not included because it differs substantially even in one unit area. - EEW is to be broadcast through the broadcasting media(TV, radio and City Administrative Disaster Management Radio), and is delivered to cellular phones through cell broadcast system. For those who would like to know the more precise estimation and smaller earthquake information at their point of their properties, JMA allows designated private companies to provide forecast of strong ground motion, in which the estimation of a seismic intensity as well as arrival time of S-wave are contained, at arbitrary places under the JMA’s technical assurance. From October, 2007 to August, 2009, JMA issued 11 warnings to general public expecting seismic intensity “5 lower” or greater, including M=7.2 inland

Nicaragua, like much of Central America, suffers from frequent damaging earthquakes (6 M7+ earthquakes occurred in the last 100 years). Thrust events occur at the Middle America Trench where the Cocos plate subducts by 72-81 mm/yr eastward beneath the Caribbean plate. Shallow crustal events occur on-shore, with potential extensive damage as demonstrated in 1972 by a M6.2 earthquake, 5 km beneath Managua. This seismotectonic setting is challenging for EarthquakeEarlyWarning (EEW) because the target events derive from both the offshore seismicity, with potentially large lead times but uncertain locations, and shallow seismicity in close proximity to densely urbanized areas, where an earlywarning would be short if available at all. Nevertheless, EEW could reduce Nicaragua's earthquake exposure. The Swiss Development and Cooperation Fund and the Nicaraguan Government have funded a collaboration between the Swiss Seismological Service (SED) at ETH Zurich and the Nicaraguan Geosciences Institute (INETER) in Managua to investigate and build a prototype EEW system for Nicaragua and the wider region. In this contribution, we present the potential of EEW to effectively alert Nicaragua and the neighbouring regions. We model alert time delays using all available seismic stations (existing and planned) in the region, as well as communication and processing delays (observed and optimal) to estimate current and potential performances of EEW alerts. Theoretical results are verified with the output from the Virtual Seismologist in SeisComP3 (VS(SC3)). VS(SC3) is implemented in the INETER SeisComP3 system for real-time operation and as an offline instance, that simulates real-time operation, to record processing delays of playback events. We compare our results with similar studies for Europe, California and New Zealand. We further highlight current capabilities and challenges for providing EEW alerts in Nicaragua. We also discuss how combining different algorithms, like e.g. VS

Early-warning of approaching strong shaking that could have fatal consequences is a research field that has made great progress. It makes it possible to reduce the impact on dangerous processes in critical facilities and on trains. However, its potential to save lives has a serious Achilles heel: The time for getting to safety is five to 10 seconds only, in many cities. Occupants of upper floors cannot get out of their buildings and narrow streets are not a safe place in strong earthquakes for people who might be able to exit. Thus, only about 10% of a city’s population can benefit from early-warnings, unless they have access to their own earthquake closet that is strong enough to remain intact in a collapsing building. Such an Earthquake Protection Unit (EPU) may be installed in the structurally strongest part of an existing apartment at low cost. In new constructions, we propose that an earthquake shelter be constructed for each floor, large enough to accommodate all occupants of that floor. These types of EPU should be constructed on top of each other, forming a strong tower, next to the elevator shaft and the staircase, at the center of the building. If an EPU with structural properties equivalent to an E-class building is placed into a building of B-class in South America, for example, we estimate that the chances of surviving shaking of intensity VII is about 30,000 times better inside the closet. The probability of escaping injury inside compared to outside we estimate as about 1,500 times better. Educating the population regarding the usefulness of EPUs will be essential, and P-waves can be used as the earlywarning signal. The owner of an earthquake closet can easily be motivated to take protective measures, when these involve simply to step into his closet, rather than attempting to exit from the building by running down many flights of stairs. Our intention is to start a discussion how best to construct EPUs and how to introduce legislation that will

The effects of earthquake shaking on the population and infrastructure across the State of Hawaii could be catastrophic, and the high seismic hazard in the region emphasizes the likelihood of such an event. Earthquakeearlywarning (EEW) has the potential to give several seconds of warning before strong shaking starts, and thus reduce loss of life and damage to property. The two approaches to EEW are (1) a network approach (such as ShakeAlert or ElarmS) where the regional seismic network is used to detect the earthquake and distribute the alarm and (2) a local approach where a critical facility has a single seismometer (or small array) and a warning system on the premises.The network approach, also referred to here as ShakeAlert or ElarmS, uses the closest stations within a regional seismic network to detect and characterize an earthquake. Most parameters used for a network approach require observations on multiple stations (typically 3 or 4), which slows down the alarm time slightly, but the alarms are generally more reliable than with single-station EEW approaches. The network approach also benefits from having stations closer to the source of any potentially damaging earthquake, so that alarms can be sent ahead to anyone who subscribes to receive the notification. Thus, a fully implemented ShakeAlert system can provide seconds of warning for both critical facilities and general populations ahead of damaging earthquake shaking.The cost to implement and maintain a fully operational ShakeAlert system is high compared to a local approach or single-station solution, but the benefits of a ShakeAlert system would be felt statewide—the warning times for strong shaking are potentially longer for most sources at most locations.The local approach, referred to herein as “single station,” uses measurements from a single seismometer to assess whether strong earthquake shaking can be expected. Because of the reliance on a single station, false alarms are more common than

In Japan, the earthquakeearlywarning system (Kinkyu Jishin Sokuhou in Japanese) maintained by the Japan Meterological Agency (JMA) has been in operation and sending pubic information since October 1, 2007. Messages have been broadcast on television and radio to warn of strong shaking to the public. The threshold for broadcasting a message is an estimated intensity of JMA 5 lower, which is approximately equivalent to MM VII to VIII. During the period from October 2007 through August 2010, messages have been sent 9 times for earthquakes of magnitude 5.2 to 7.0. There have been a few instances of significantly over-estimating or under-estimating the predicted shaking, but in general the performance of the system has been quite good. The quality of the detection system depends on the dense network of high-quality seismometers that cover the Japanese Islands. Consequently, the system works very well for events on or close to the 4 main islands, but there is more uncertainty for events near the smaller and more distant islands where the density of instrumentation is much less The EarlyWarning System is also tied to an extensive education program so that the public can react appropriately in the short amount of time given by the warning. There appears to be good public support in Japan, where people have become accustomed to a high level of fast information on a daily basis. There has also been development of a number of specific safety applications in schools and industry that work off the backbone information provided in the national system.

With more than 25 million people at risk from high hazard faults in California alone, EarthquakeEarlyWarning (EEW) presents a promising public safety and emergency response tool. EEW represents the real-time end of an earthquake information spectrum which also includes near real-time notifications of earthquake location, magnitude, and shaking levels; as well as geographic information system (GIS)-based products for compiling and visually displaying processed earthquake data such as ShakeMap and ShakeCast. Improvements to and increased multi-national implementation of EEW have stimulated interest in how such information products could be used in the future. Lifeline organizations, consisting of utilities and transportation systems, can use both onsite and regional EEW information as part of their risk management and public safety programs. Regional EEW information can provide improved situational awareness to system operators before automatic system protection devices activate, and allow trained personnel to take precautionary measures. On-site EEW is used for earthquake-actuated automatic gas shutoff valves, triggered garage door openers at fire stations, system controls, etc. While there is no public policy framework for preemptive, precautionary electricity or gas service shutdowns by utilities in the United States, gas shut-off devices are being required at the building owner level by some local governments. In the transportation sector, high-speed rail systems have already demonstrated the ‘proof of concept’ for EEW in several countries, and more EEW systems are being installed. Recently the Bay Area Rapid Transit District (BART) began collaborating with the California Integrated Seismic Network (CISN) and others to assess the potential benefits of EEW technology to mass transit operations and emergency response in the San Francisco Bay region. A key issue in this assessment is that significant earthquakes are likely to occur close to or within the BART

Earthquakeearlywarning (EEW) system has already been developed and tested in Taiwan for more than ten years. With the implementation of a real-time strong-motion network by the Central Weather Bureau (CWB), a virtual sub-network (VSN) system based on regional earlywarning approach was utilized at the first attempt. In order to shorten the processing time, seismic waveforms in a 10-sec time window starting from the first P-wave arrival time at the nearest station are used to determine the hypocenter and earthquake magnitude which is dubbed ML10. Since 2001, this EEW system has responded to a total of 255 events with magnitude greater than 4.5 occurred inland or off the coast of Taiwan. The system is capable of issuing an earthquake report within 20 sec of its occurrence with good magnitude estimations for events up to magnitude 6.5. This will provide earlywarning for metropolitan areas located 70 km away from the epicentre. In the latest development, a new prototype EEW system based on P-wave method was developed. Instead of ML10, we adopt the “Pd magnitude”, MPd, as our magnitude indicator in the new system. Pd is defined as the peak amplitude of the initial P-wave displacement. In the previous studies, by analyzing the Pd attenuation relationship with earthquake magnitudes, Pd was proved to be a good magnitude estimator for EEW purpose. Therefore, we adopt the Pd magnitude in developing our next generation EEW system. The new system is designed and constructed based on the Central Weather Bureau Seismographic Network (CWBSN). The CWBSN is a real-time seismographic network with more than one hundred digital telemetered seismic stations distributed over the entire Taiwan. Currently, there are three types of seismic instruments installed at the stations, either co-site or separately installed, including short-period seismographs, accelerometers, and broadband instruments. For the need of integral data processing, we use the Earthworm system as a common

Earthquake magnitude is a concise metric that provides invaluable information about the destructive potential of a seismic event. Rapid estimation of magnitude for earthquake and tsunami earlywarning purposes requires reliance on near-field instrumentation. For large magnitude events, ground motions can exceed the dynamic range of near-field broadband seismic instrumentation (clipping). Strong motion accelerometers are designed with low gains to better capture strong shaking. Estimating earthquake magnitude rapidly from near-source strong-motion data requires integration of acceleration waveforms to displacement. However, integration amplifies small errors, creating unphysical drift that must be eliminated with a high pass filter. The loss of the long period information due to filtering is an impediment to magnitude estimation in real-time; the relation between ground motion measured with strong-motion instrumentation and magnitude saturates, leading to underestimation of earthquake magnitude. Using station displacements from Global Navigation Satellite System (GNSS) observations, we can supplement the high frequency information recorded by traditional seismic systems with long-period observations to better inform rapid response. Unlike seismic-only instrumentation, ground motions measured with GNSS scale with magnitude without saturation [Crowell et al., 2013; Melgar et al., 2015]. We refine the current magnitude scaling relations using peak ground displacement (PGD) by adding a large GNSS dataset of earthquakes in Japan. Because it does not suffer from saturation, GNSS alone has significant advantages over seismic-only instrumentation for rapid magnitude estimation of large events. The earthquake's magnitude can be estimated within 2-3 minutes of earthquake onset time [Melgar et al., 2013]. We demonstrate that seismogeodesy, the optimal combination of GNSS and seismic data at collocated stations, provides the added benefit of improving the sensitivity of

Earthquakeearlywarning (EEW) systems are one of the most effective ways to reduce earthquake disaster. Earthquake magnitude estimation is one of the most important and also the most difficult parts of the entire EEW system. In this paper, based on 142 earthquake events and 253 seismic records that were recorded by the KiK-net in Japan, and aftershocks of the large Wenchuan earthquake in Sichuan, we obtained earthquake magnitude estimation relationships using the τ c and P d methods. The standard variances of magnitude calculation of these two formulas are ±0.65 and ±0.56, respectively. The P d value can also be used to estimate the peak ground motion of velocity, then warning information can be released to the public rapidly, according to the estimation results. In order to insure the stability and reliability of magnitude estimation results, we propose a compatibility test according to the natures of these two parameters. The reliability of the earlywarning information is significantly improved though this test.

The City of Los Angeles has been involved in the testing of the Cal Tech Shake Alert, EarthquakeEarlyWarning (EQEW) system, since February 2012. This system accesses a network of seismic monitors installed throughout California. The system analyzes and processes seismic information, and transmits a warning (audible and visual) when an earthquake occurs. In late 2011, the City of Los Angeles Emergency Management Department (EMD) was approached by Cal Tech regarding EQEW, and immediately recognized the value of the system. Simultaneously, EMD was in the process of finalizing a report by a multi-discipline team that visited Japan in December 2011, which spoke to the effectiveness of EQEW for the March 11, 2011 earthquake that struck that country. Information collected by the team confirmed that the EQEW systems proved to be very effective in alerting the population of the impending earthquake. The EQEW in Japan is also tied to mechanical safeguards, such as the stopping of high-speed trains. For a city the size and complexity of Los Angeles, the implementation of a reliable EQEW system will save lives, reduce loss, ensure effective and rapid emergency response, and will greatly enhance the ability of the region to recovery from a damaging earthquake. The current Shake Alert system is being tested at several governmental organizations and private businesses in the region. EMD, in cooperation with Cal Tech, identified several locations internal to the City where the system would have an immediate benefit. These include the staff offices within EMD, the Los Angeles Police Department's Real Time Analysis and Critical Response Division (24 hour crime center), and the Los Angeles Fire Department's Metropolitan Fire Communications (911 Dispatch). All three of these agencies routinely manage the collaboration and coordination of citywide emergency information and response during times of crisis. Having these three key public safety offices connected and included in the

A discussion of P- and S-waves seems an ubiquitous part of studying earthquakes in the classroom. Textbooks from middle school through university level typically define the differences between the waves and illustrate the sense of motion. While many students successfully memorize the differences between wave types (often utilizing the first letter as a memory aide), textbooks rarely give tangible examples of how the two waves would "feel" to a person sitting on the ground. One reason for introducing the wave types is to explain how to calculate earthquake epicenters using seismograms and travel time charts -- very abstract representations of earthquakes. Even when the skill is mastered using paper-and-pencil activities or one of the excellent online interactive versions, locating an epicenter simply does not excite many of our students because it evokes little emotional impact, even in students located in earthquake-prone areas. Despite these limitations, huge numbers of students are mandated to complete the task. At the K-12 level, California requires that all students be able to locate earthquake epicenters in Grade 6; in New York, the skill is a required part of the Regent's Examination. Recent innovations in earthquakeearlywarning systems around the globe give us the opportunity to address the same content standard, but with substantially more emotional impact on students. I outline a lesson about earthquakes focused on earthquakeearlywarning systems. The introductory activities include video clips of actual earthquakes and emphasize the differences between the way P- and S-waves feel when they arrive (P arrives first, but is weaker). I include an introduction to the principle behind earthquakeearlywarning (including a summary of possible uses of a few seconds warning about strong shaking) and show examples from Japan. Students go outdoors to simulate P-waves, S-waves, and occupants of two different cities who are talking to one another on cell phones

Earlywarning is a warning mechanism before an actual incident occurs, can be implemented on natural events such as tsunamis or earthquakes. Earthquakes are classified in tectonic and volcanic types depend on the source and nature. The tremor in the form of energy propagates in all directions as Primary and Secondary waves. Primary wave as initial earthquake vibrations propagates longitudinally, while the secondary wave propagates like as a sinusoidal wave after Primary, destructive and as a real earthquake. To process the primary vibration data captured by the earthquake sensor, a network management required client computer to receives primary data from sensors, authenticate and forward to a server computer to set up an earlywarning system. With the water propagation concept, a method of earlywarning system has been determined in which some sensors are located on the same line, sending initial vibrations as primary data on the same scale and the server recommended to the alarm sound as an earlywarning.

CISN ShakeAlert is a prototype earthquakeearlywarning system being developed and tested by the California Integrated Seismic Network. The system has recently been expanded to support redundant data processing and communications. It now runs on six machines at three locations with ten Apache ActiveMQ message brokers linking together 18 waveform processors, 12 event association processes and 4 Decision Module alert processes. The system ingests waveform data from about 500 stations and generates many thousands of triggers per day, from which a small portion produce earthquake alerts. We have developed interactive web browser system-monitoring tools that display near real time state-of-health and performance information. This includes station availability, trigger statistics, communication and alert latencies. Connections to regional earthquake catalogs provide a rapid assessment of the Decision Module hypocenter accuracy. Historical performance can be evaluated, including statistics for hypocenter and origin time accuracy and alert time latencies for different time periods, magnitude ranges and geographic regions. For the ElarmS event associator, individual earthquake processing histories can be examined, including details of the transmission and processing latencies associated with individual P-wave triggers. Individual station trigger and latency statistics are available. Detailed information about the ElarmS trigger association process for both alerted events and rejected events is also available. The Google Web Toolkit and Map API have been used to develop interactive web pages that link tabular and geographic information. Statistical analysis is provided by the R-Statistics System linked to a PostgreSQL database.

Since 2014 the Earthworm Based Earthquake Alarm Reporting (eBEAR) system has been operated and been used to issue warnings to schools. In 2015 the system started to provide warnings to the public in Taiwan via television and the cell phone. Online performance of the eBEAR system indicated that the average reporting times afforded by the system are approximately 15 and 28 s for inland and offshore earthquakes, respectively. The eBEAR system in average can provide more warning time than the current EEW system (3.2 s and 5.5 s for inland and offshore earthquakes, respectively). However, offshore earthquakes were usually located poorly because only P-wave arrivals were used in the eBEAR system. Additionally, in the early stage of the earthquakeearlywarning system, only fewer stations are available. The poor station coverage may be a reason to answer why offshore earthquakes are difficult to locate accurately. In the Geiger's inversion procedure of earthquake location, we need to put an initial hypocenter and origin time into the location program. For the initial hypocenter, we defined some test locations on the offshore area instead of using the average of locations from triggered stations. We performed 20 programs concurrently running the Geiger's method with different pre-defined initial position to locate earthquakes. We assume that if the program with the pre-defined initial position is close to the true earthquake location, during the iteration procedure of the Geiger's method the processing time of this program should be less than others. The results show that using pre-defined locations for trial-hypocenter in the inversion procedure is able to improve the accurate of offshore earthquakes. Especially for EEW system, in the initial stage of the EEW system, only use 3 or 5 stations to locate earthquakes may lead to bad results because of poor station coverage. In this study, the pre-defined trial-locations provide a feasible way to improve the estimations of

up to 65 km away. Our analysis shows that existing fiber optic installations along infrastructure could be combined to form a large aperture array with tens of thousands of channels for epicenter estimation and for earlywarning purposes, augmenting existing earthquake sensor networks.

Implementing EarthquakeEarlyWarning System (EEWS) triggering algorithms into seismic networks has been a hot topic of discussion for some years now. With digitizer technology now available, such as the Güralp Minimus, with on average 40-60ms delay time (latency) from earthquake origin to issuing an alert the next step is to provide network operators with a simple interface for on board parameter calculations from a seismic station. A voting mechanism is implemented on board which mitigates the risk of false positives being communicated. Each Minimus can be configured to with a `score' from various sources i.e. Z channel on seismometer, N/S E/W channels on accelerometer and MEMS inside Minimus. If the score exceeds the set threshold then an alert is sent to the `Master Minimus'. The Master Minimus within the network will also be configured as to when the alert should be issued i.e. at least 3 stations must have triggered. Industry standard algorithms focus around the calculation of Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD) and C. Calculating these single station parameters on-board in order to stream only the results could help network operators with possible issues, such as restricted bandwidth. Developments on the Minimus allow these parameters to be calculated and distributed through Common Alert Protocol (CAP). CAP is the XML based data format used for exchanging and describing public warnings and emergencies. Whenever the trigger conditions are met the Minimus can send a signed UDP packet to the configured CAP receiver which can then send the alert via SMS, e-mail or CAP forwarding. Increasing network redundancy is also a consideration when developing these features, therefore the forwarding CAP message can be sent to multiple destinations. This allows for a hierarchical approach by which the single station (or network) parameters can be streamed to another Minimus, or data centre, or both, so that there is no

Earthquakeearlywarning systems use earthquake science and the technology of monitoring systems to alert devices and people when shaking waves generated by an earthquake are expected to arrive at their location. The seconds to minutes of advance warning can allow people and systems to take actions to protect life and property from destructive shaking. The U.S. Geological Survey (USGS), in collaboration with several partners, has been working to develop an earlywarning system for the United States. ShakeAlert, a system currently under development, is designed to cover the West Coast States of California, Oregon, and Washington.

Although variants of both earthquakeearlywarning and short-term operational earthquake forecasting systems have been implemented or are now being implemented in some regions and nations, they have been slow to gain acceptance within the disciplines that produced them as well as among those for whom they were intended to assist. To accelerate the development and implementation of these technologies will require the cooperation and collaboration of multiple disciplines, some inside and others outside of academia. Seismologists, social scientists, emergency managers, elected officials and key opinion leaders from the media and public must be the participants in this process. Representatives of these groups come from both inside and outside of academia and represent very different organizational cultures, backgrounds and expectations for these systems, sometimes leading to serious disagreements and impediments to further development and implementation. This presentation will focus on examples of the emergence of earthquakeearlywarning and operational earthquake forecasting systems in California, Japan and other regions and document the challenges confronted in the ongoing effort to improve seismic safety.

The basic physics of earthquakes is such that strong ground motion cannot be expected from an earthquake unless the earthquake itself is very close or has grown to be very large. We use simple seismological relationships to calculate the minimum time that must elapse before such ground motion can be expected at a distance from the earthquake, assuming that the earthquake magnitude is not predictable. Earthquakeearlywarning (EEW) systems are in operation or development for many regions around the world, with the goal of providing enough warning of incoming ground shaking to allow people and automated systems to take protective actions to mitigate losses. However, the question of how much warning time is physically possible for specified levels of ground motion has not been addressed. We consider a zero-latency EEW system to determine possible warning times a user could receive in an ideal case. In this case, the only limitation on warning time is the time required for the earthquake to evolve and the time for strong ground motion to arrive at a user’s location. We find that users who wish to be alerted at lower ground motion thresholds will receive more robust warnings with longer average warning times than users who receive warnings for higher ground motion thresholds. EEW systems have the greatest potential benefit for users willing to take action at relatively low ground motion thresholds, whereas users who set relatively high thresholds for taking action are less likely to receive timely and actionable information.

The basic physics of earthquakes is such that strong ground motion cannot be expected from an earthquake unless the earthquake itself is very close or has grown to be very large. We use simple seismological relationships to calculate the minimum time that must elapse before such ground motion can be expected at a distance from the earthquake, assuming that the earthquake magnitude is not predictable. Earthquakeearlywarning (EEW) systems are in operation or development for many regions around the world, with the goal of providing enough warning of incoming ground shaking to allow people and automated systems to take protective actions to mitigate losses. However, the question of how much warning time is physically possible for specified levels of ground motion has not been addressed. We consider a zero-latency EEW system to determine possible warning times a user could receive in an ideal case. In this case, the only limitation on warning time is the time required for the earthquake to evolve and the time for strong ground motion to arrive at a user’s location. We find that users who wish to be alerted at lower ground motion thresholds will receive more robust warnings with longer average warning times than users who receive warnings for higher ground motion thresholds. EEW systems have the greatest potential benefit for users willing to take action at relatively low ground motion thresholds, whereas users who set relatively high thresholds for taking action are less likely to receive timely and actionable information. PMID:29750190

A panel was established to investigate the subject of real-time earthquake monitoring (RTEM) and suggest recommendations on the feasibility of using a real-time earthquakewarning system to mitigate earthquake damage in regions of the United States. The findings of the investigation and the related recommendations are described in this report. A brief review of existing real-time seismic systems is presented with particular emphasis given to the current California seismic networks. Specific applications of a real-time monitoring system are discussed along with issues related to system deployment and technical feasibility. In addition, several non-technical considerations are addressed including cost-benefit analysis, public perceptions, safety, and liability.

GNSS-based earthquakeearlywarning (EEW) algorithms estimate fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because large events are infrequent, algorithms are not regularly exercised and insufficiently tested on few available datasets. The Geodetic Alarm System (G-larmS) is a GNSS-based finite-fault algorithm developed as part of the ShakeAlert EEW system in the western US. Performance evaluations using synthetic earthquakes offshore Cascadia showed that G-larmS satisfactorily recovers magnitude and fault length, providing useful alerts 30-40 s after origin time and timely warnings of ground motion for onshore urban areas. An end-to-end test of the ShakeAlert system demonstrated the need for GNSS data to accurately estimate ground motions in real-time. We replay real data from several subduction-zone earthquakes worldwide to demonstrate the value of GNSS-based EEW for the largest, most damaging events. We compare predicted ground acceleration (PGA) from first-alert-solutions with those recorded in major urban areas. In addition, where applicable, we compare observed tsunami heights to those predicted from the G-larmS solutions. We show that finite-fault inversion based on GNSS-data is essential to achieving the goals of EEW.

The Caribbean region (CR) has a documented history of large damaging earthquakes and tsunamis that have affected coastal areas, including the events of Jamaica in 1692, Virgin Islands in 1867, Puerto Rico in 1918, the Dominican Republic in 1946 and Haiti in 2010. There is clear evidence that tsunamis have been triggered by large earthquakes that deformed the ocean floor around the Caribbean Plate boundary. The CR is monitored jointly by national/regional/local seismic, geodetic and sea level networks. All monitoring institutions are participating in the UNESCO ICG/Caribe EWS, the purpose of this initiative is to minimize loss of life and destruction of property, and to mitigate against catastrophic economic impacts via promoting local research, real time (RT) earthquake, geodetic and sea level data sharing and improving warning capabilities and enhancing education and outreach strategies. Currently more than, 100 broad-band seismic, 65 sea levels and 50 GPS high rate stations are available in real or near real-time. These real-time streams are used by Local/Regional or Worldwide detection and warning institutions to provide earthquake source parameters in a timely manner. Currently, any Caribbean event detected to have a magnitude greater than 4.5 is evaluated, and sea level is measured, by the TWC for tsumanigenic potential. The regional cooperation is motivated both by research interests as well as geodetic, seismic and tsunami hazard monitoring and warning. It will allow the imaging of the tectonic structure of the Caribbean region to a high resolution which will consequently permit further understanding of the seismic source properties for moderate and large events and the application of this knowledge to procedures of civil protection. To reach its goals, the virtual network has been designed following the highest technical standards: BB sensors, 24 bits A/D converters with 140 dB dynamic range, real-time telemetry. Here we will discuss the state of the PR

EarthquakeEarlyWarning System (EEWS) is an effective approach to mitigate earthquake damage. In this study, we used the seismic record by the Kiban Kyoshin network (KiK-net), because it has dense station coverage and co-located borehole strong-motion seismometers along with the free-surface strong-motion seismometers. We used inland earthquakes with moment magnitude (Mw) from 5.0 to 7.3 between 1998 and 2012. We choose 135 events and 10950 strong ground accelerograms recorded by the 696 strong ground accelerographs. Both the free-surface and the borehole data are used to calculate τc and Pd, respectively. The results show that τc*Pd has a good correlation with PGV and is a robust parameter for assessing the potential of damaging earthquake. We propose the value of τc*Pd determined from seconds after the arrival of P wave could be a threshold for the on-site type of EEW.

The ultimate goal of earthquakeearlywarning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

economic repercussion. We provide the school kids with the "World Seismicity Map" to let them realize that earthquake disasters take place unequally. Then we let the kids jump in front of the seismometer with projecting the real-time data to the wall. Grouped kids contest the largest amplitude by carefully considering how to jump high but nail the landing with their teammates. Their jumps are printed out via portable printer and compared with the real earthquake which occurred even 600km away but still huge when printed out in the same scale. Actually, a magnitude 7 earthquake recorded 600km away needs an A0 paper when scaled with a jump of 10 kids printed in an A4 paper. They've got to understand what to do not to be killed with the great big energy. We also offer earthquake drills using the EarthquakeEarlyWarning System (EEW System). An EEW System is officially introduced in 2007 by JMA (Japan Meteorological Agency) to issue prompt alerts to provide several to several ten seconds before S-wave arrives. When hearing the alarm, school kids think fast to find a place to protect themselves. It is not always when they are in their classrooms but in the chemical lab, music room which does not have any desks to protect them, or in the PE class. Then in the science class, we demonstrate how the EEW System works. A 8m long wave propagation device made with spindles connected with springs is used to visualize the P- and S-waves. In the presentation, we would like to show the paper materials and sufficient movies.

An earthquakeearlywarning (EEW) system with integration of regional and onsite approaches was installed at nine demonstration stations in several districts of Taiwan for taking advantages of both approaches. The system performance was evaluated by a 3-year experiment at schools, which experienced five major earthquakes during this period. The blind zone of warning was effectively reduced by the integrated EEW system. The predicted intensities from EEW demonstration stations showed acceptable accuracy compared to field observations. The operation experience from an earthquake event proved that students could calmly carry out correct action before the seismic wave arrived using some warning time provided by the EEW system. Through successful operation in practice, the integrated EEW system was verified as an effective tool for disaster prevention at schools.

Earthquakeearlywarning is an important and challenging issue for the reduction of the seismic damage, especially for the mitigation of human suffering. One of the most important problems in earthquakeearlywarning systems is how immediately we can estimate the final size of an earthquake after we observe the ground motion. It is relevant to the problem whether the initial rupture of an earthquake has some information associated with its final size. Nakamura (1988) developed the Urgent Earthquake Detection and Alarm System (UrEDAS). It calculates the predominant period of the P wave (τp) and estimates the magnitude of an earthquake immediately after the P wave arrival from the value of τpmax, or the maximum value of τp. The similar approach has been adapted by other earthquake alarm systems (e.g., Allen and Kanamori (2003)). To investigate the characteristic of the parameter τp and the effect of the length of the time window (TW) in the τpmax calculation, we analyze the high-frequency recordings of earthquakes at very close distances in the Mponeng mine in South Africa. We find that values of τpmax have upper and lower limits. For larger earthquakes whose source durations are longer than TW, the values of τpmax have an upper limit which depends on TW. On the other hand, the values for smaller earthquakes have a lower limit which is proportional to the sampling interval. For intermediate earthquakes, the values of τpmax are close to their typical source durations. These two limits and the slope for intermediate earthquakes yield an artificial final size dependence of τpmax in a wide size range. The parameter τpmax is useful for detecting large earthquakes and broadcasting earthquakeearlywarnings. However, its dependence on the final size of earthquakes does not suggest that the earthquake rupture is deterministic. This is because τpmax does not always have a direct relation to the physical quantities of an earthquake.

Large magnitude earthquakes in urban environments continue to kill and injure tens to hundreds of thousands of people, inflicting lasting societal and economic disasters. Earthquakeearlywarning (EEW) provides seconds to minutes of warning, allowing people to move to safe zones and automated slowdown and shutdown of transit and other machinery. The handful of EEW systems operating around the world use traditional seismic and geodetic networks that exist only in a few nations. Smartphones are much more prevalent than traditional networks and contain accelerometers that can also be used to detect earthquakes. We report on the development of a new type of seismic system, MyShake, that harnesses personal/private smartphone sensors to collect data and analyze earthquakes. We show that smartphones can record magnitude 5 earthquakes at distances of 10 km or less and develop an on-phone detection capability to separate earthquakes from other everyday shakes. Our proof-of-concept system then collects earthquake data at a central site where a network detection algorithm confirms that an earthquake is under way and estimates the location and magnitude in real time. This information can then be used to issue an alert of forthcoming ground shaking. MyShake could be used to enhance EEW in regions with traditional networks and could provide the only EEW capability in regions without. In addition, the seismic waveforms recorded could be used to deliver rapid microseism maps, study impacts on buildings, and possibly image shallow earth structure and earthquake rupture kinematics. PMID:26933682

Large magnitude earthquakes in urban environments continue to kill and injure tens to hundreds of thousands of people, inflicting lasting societal and economic disasters. Earthquakeearlywarning (EEW) provides seconds to minutes of warning, allowing people to move to safe zones and automated slowdown and shutdown of transit and other machinery. The handful of EEW systems operating around the world use traditional seismic and geodetic networks that exist only in a few nations. Smartphones are much more prevalent than traditional networks and contain accelerometers that can also be used to detect earthquakes. We report on the development of a new type of seismic system, MyShake, that harnesses personal/private smartphone sensors to collect data and analyze earthquakes. We show that smartphones can record magnitude 5 earthquakes at distances of 10 km or less and develop an on-phone detection capability to separate earthquakes from other everyday shakes. Our proof-of-concept system then collects earthquake data at a central site where a network detection algorithm confirms that an earthquake is under way and estimates the location and magnitude in real time. This information can then be used to issue an alert of forthcoming ground shaking. MyShake could be used to enhance EEW in regions with traditional networks and could provide the only EEW capability in regions without. In addition, the seismic waveforms recorded could be used to deliver rapid microseism maps, study impacts on buildings, and possibly image shallow earth structure and earthquake rupture kinematics.

The importance of GNSS-based earthquakeearlywarning for modeling large earthquakes has been studied extensively over the past decade and several such systems are currently under development. In the Pacific Northwest, we have developed the G-FAST GNSS-based earthquakeearlywarning module for eventual inclusion in the US West-Coast wide ShakeAlert system. We have also created a test system that allows us to replay past and synthetic earthquakes to identify problems with both the network architecture and the algorithms. Between 2010 and 2016, there have been seven M > 8 earthquakes across the globe, of which three struck offshore Chile; the 27 February 2010 Mw 8.8 Maule, the 1 April 2014 Mw 8.2 Iquique, and the 16 September 2015 Mw 8.3 Illapel. Subsequent to these events, the Chilean national GNSS network operated by the Centro Sismologico Nacional (http://www.sismologia.cl/) greatly expanded to over 150 continuous GNSS stations, providing the best recordings of great earthquakes with GNSS outside of Japan. Here we report on retrospective G-FAST performance for those three great earthquakes in Chile. We discuss the interplay of location errors, latency, and data completeness with respect to the precision and timing of G-FAST earthquake source alerts as well as the computational demands of the system.

The National Taiwan University (NTU) developed an earthquakeearlywarning (EEW) system for research purposes using low-cost accelerometers (P-Alert) since 2010. As of 2017, a total of 650 stations have been deployed and configured. The NTU system can provide earthquake information within 15 s of an earthquake occurrence. Thus, this system may provide earlywarnings for cities located more than 50 km from the epicenter. Additionally, the NTU system also has an onsite alert function that triggers a warning for incoming P-waves greater than a certain magnitude threshold, thus providing a 2-3 s lead time before peak ground acceleration (PGA) for regions close to an epicenter. Detailed shaking maps are produced by the NTU system within one or two minutes after an earthquake. Recently, a new module named ShakeAlarm has been developed. Equipped with real-time acceleration signals and the time-dependent anisotropic attenuation relationship of the PGA, ShakingAlarm can provide an accurate PGA estimation immediately before the arrival of the observed PGA. This unique advantage produces sufficient lead time for hazard assessment and emergency response, which is unavailable for traditional shakemap, which are based on only the PGA observed in real time. The performance of ShakingAlarm was tested with six M > 5.5 inland earthquakes from 2013 to 2016. Taking the 2016 M6.4 Meinong earthquake simulation as an example, the predicted PGA converges to a stable value and produces a predicted shake map and an isocontour map of the predicted PGA within 16 seconds of earthquake occurrence. Compared with traditional regional EEW system, ShakingAlarm can effectively identify possible damage regions and provide valuable earlywarning information (magnitude and PGA) for risk mitigation.

We will report the results of a questionnaire survey on EarthquakeEarlyWarning (EEW), conducted by the Japan Meteorological Agency (JMA) in February 2012, approximately one year after the 2011 off the Pacific coast of Tohoku Earthquake (Mw9.0). In the questionnaire survey, which is based on the performance of the 5-year operation of EEW, the respondents were asked how they obtained EEW, how they reacted to EEW and how useful they considered EEW as a safety measure against strong ground shaking. Respondents numbered 817 in the Tohoku district survey and 2,000 in the nationwide survey. Most respondents received EEW messages from TV or cell phone broadcast mail service. Most respondents took some actions in the Tohoku district (74 percent) and nationwide (54 percent); 16 and 17 percent, respectively, tried to take action but could not; and 10 and 29 percent, respectively, did nothing. More than 90 and 80 percent of respondents thought EEW was useful in the Tohoku district and nationwide, respectively. Many people stated that EEW helped them prepare for strong shaking, even if they did not actually take specific actions. The percentage of respondents evaluating EEW to be useful was larger among Tohoku than nationwide. Likewise, the percentage of people who were able to take useful actions was larger in the Tohoku than nationwide. The difference may be attributed to the degree of experience of EEW that had been frequently issued particularly to the Tohoku district since March the 11th 2011. The benefit of the EEW system was recognized both as a trigger of taking actual actions and as an aid to mental preparedness before strong jolts began. Most people considered that the EEW system was useful despite of some false alarms. Although it is necessary to improve the EEW system to reduce false alarms and make the predictions more precise, the results of this survey should be encouraging to the community of promoting and researching EEW.

A prototype earthquakeearlywarning (EEW) system is currently in development in the Pacific Northwest. We have taken a two‐stage approach to EEW: (1) detection and initial characterization using strong‐motion data with the Earthquake Alarm Systems (ElarmS) seismic earlywarning package and (2) the triggering of geodetic modeling modules using Global Navigation Satellite Systems data that help provide robust estimates of large‐magnitude earthquakes. In this article we demonstrate the performance of the latter, the Geodetic First Approximation of Size and Time (G‐FAST) geodetic earlywarning system, using simulated displacements for the 2001Mw 6.8 Nisqually earthquake. We test the timing and performance of the two G‐FAST source characterization modules, peak ground displacement scaling, and Centroid Moment Tensor‐driven finite‐fault‐slip modeling under ideal, latent, noisy, and incomplete data conditions. We show good agreement between source parameters computed by G‐FAST with previously published and postprocessed seismic and geodetic results for all test cases and modeling modules, and we discuss the challenges with integration into the U.S. Geological Survey’s ShakeAlert EEW system.

Ocean Networks Canada (ONC — oceannetworks.ca/ ) operates cabled ocean observatories off the coast of British Columbia (BC) to support research and operational oceanography. Recently, ONC has been funded by the Province of BC to deliver an earthquakeearlywarning (EEW) system that integrates offshore and land-based sensors to deliver alerts of incoming ground shaking from the Cascadia Subduction Zone. ONC's cabled seismic network has the unique advantage of being located offshore on either side of the surface expression of the subduction zone. The proximity of ONC's sensors to the fault can result in faster, more effective warnings, which translates into more lives saved, injuries avoided and more ability for mitigative actions to take place.ONC delivers near real-time data from various instrument types simultaneously, providing distinct advantages to seismic monitoring and earthquakeearlywarning. The EEW system consists of a network of sensors, located on the ocean floor and on land, that detect and analyze the initial p-wave of an earthquake as well as the crustal deformation on land during the earthquake sequence. Once the p-wave is detected and characterized, software systems correlate the data streams of the various sensors and deliver alerts to clients through a Common Alerting Protocol-compliant data package. This presentation will focus on the development of the earthquakeearlywarning capacity at ONC. It will describe the seismic sensors and their distribution, the p-wave detection algorithms selected and the overall architecture of the system. It will further overview the plan to achieve operational readiness at project completion.

We have introduced recently new methods to determine rapidly the tsunami potential and magnitude of large earthquakes (e.g., Lomax and Michelini, 2009ab, 2011, 2012). To validate these methods we have implemented them along with other new algorithms within the Early-est earthquake monitor at INGV-Rome (http://early-est.rm.ingv.it, http://early-est.alomax.net). Early-est is a lightweight software package for real-time earthquake monitoring (including phase picking, phase association and event detection, location, magnitude determination, first-motion mechanism determination, ...), and for tsunami earlywarning based on discriminants for earthquake tsunami potential. In a simulation using archived broadband seismograms for the devastating M9, 2011 Tohoku earthquake and tsunami, Early-est determines: the epicenter within 3 min after the event origin time, discriminants showing very high tsunami potential within 5-7 min, and magnitude Mwpd(RT) 9.0-9.2 and a correct shallow-thrusting mechanism within 8 min. Real-time monitoring with Early-est givess similar results for most large earthquakes using currently available, real-time seismogram data. Here we summarize some of the key algorithms within Early-est that enable rapid, real-time earthquake monitoring and tsunami earlywarning worldwide: >>> FilterPicker - a general purpose, broad-band, phase detector and picker (http://alomax.net/FilterPicker); >>> Robust, simultaneous association and location using a probabilistic, global-search; >>> Period-duration discriminants TdT0 and TdT50Ex for tsunami potential available within 5 min; >>> Mwpd(RT) magnitude for very large earthquakes available within 10 min; >>> Waveform P polarities determined on broad-band displacement traces, focal mechanisms obtained with the HASH program (Hardebeck and Shearer, 2002); >>> SeisGramWeb - a portable-device ready seismogram viewer using web-services in a browser (http://alomax.net/webtools/sgweb/info.html). References (see also: http

In this article, the seismic records of Japan's Kik-net are selected to measure the acceleration, displacement, and effective peak acceleration of each seismic record within a certain time after P wave, then a continuous estimation is given on earthquakeearlywarning magnitude through statistical analysis method, and Wenchuan earthquake record is utilized to check the method. The results show that the reliability of earthquakeearlywarning magnitude continuously increases with the increase of the seismic information, the biggest residual happens if the acceleration is adopted to fit earthquake magnitude, which may be caused by rich high-frequency components and large dispersion of peak value in acceleration record, the influence caused by the high-frequency components can be effectively reduced if the effective peak acceleration and peak displacement is adopted, it is estimated that the dispersion of earthquake magnitude obviously reduces, but it is easy for peak displacement to be affected by long-period drifting. In various components, the residual enlargement phenomenon at vertical direction is almost unobvious, thus it is recommended in this article that the effective peak acceleration at vertical direction is preferred to estimate earthquakeearlywarning magnitude. Through adopting Wenchuan strong earthquake record to check the method mentioned in this article, it is found that this method can be used to quickly, stably, and accurately estimate the earlywarning magnitude of this earthquake, which shows that this method is completely applicable for earthquakeearlywarning.

We explore the use of collocated GPS and seismic sensors for earthquake monitoring and earlywarning. The GPS and seismic data collected during the 2011 Tohoku-Oki (Japan) and the 2010 El Mayor-Cucapah (Mexico) earthquakes are analyzed by using a tightly-coupled integration. The performance of the integrated results is validated by both time and frequency domain analysis. We detect the P-wave arrival and observe small-scale features of the movement from the integrated results and locate the epicenter. Meanwhile, permanent offsets are extracted from the integrated displacements highly accurately and used for reliable fault slip inversion and magnitude estimation. PMID:24284765

Development of space geodetic techniques such as Global Navigation Satellite System and Synthetic Aperture Radar in last few decades allows us to monitor deformation of Earth's surface in unprecedented spatial and temporal resolution. These observations, combined with fast data transmission and quick data processing, enable us to quickly detect and locate earthquakes and volcanic eruptions and assess potential hazards such as strong earthquake shaking, tsunamis, and volcanic eruptions. These techniques thus are key parts of earlywarning systems, help identify some hazards before a cataclysmic event, and improve the response to the consequent damage.

The first real-time digital strong-motion network in Central Asia has been installed in the Kyrgyz Republic since 2014. Although this network consists of only 19 strong-motion stations, they are located in near-optimal locations for earthquakeearlywarning and rapid response purposes. In fact, it is expected that this network, which utilizes the GFZ-Sentry software, allowing decentralized event assessment calculations, not only will provide useful strong motion data useful for improving future seismic hazard and risk assessment, but will serve as the backbone for regional and on-site earthquakeearlywarning operations. Based on the location of these stations, and travel-time estimates for P- and S-waves, we have determined potential lead times for several major urban areas in Kyrgyzstan (i.e., Bishkek, Osh, and Karakol) and Kazakhstan (Almaty), where we find the implementation of an efficient earthquakeearlywarning system would provide lead times outside the blind zone ranging from several seconds up to several tens of seconds. This was confirmed by the simulation of the possible shaking (and intensity) that would arise considering a series of scenarios based on historical and expected events, and how they affect the major urban centres. Such lead times would allow the instigation of automatic mitigation procedures, while the system as a whole would support prompt and efficient actions to be undertaken over large areas.

Large earthquakes, such as the Mw 7.7 1992 Nicaragua earthquake, have occurred off the Pacific coasts of El Salvador and Nicaragua in Central America and have generated distractive tsunamis along these coasts. It is necessary to determine appropriate fault models before large tsunamis hit the coast. In this study, first, fault parameters were estimated from the W-phase inversion, and then an appropriate fault model was determined from the fault parameters and scaling relationships with a depth dependent rigidity. The method was tested for four large earthquakes, the 1992 Nicaragua tsunami earthquake (Mw7.7), the 2001 El Salvador earthquake (Mw7.7), the 2004 El Astillero earthquake (Mw7.0), and the 2012 El Salvador-Nicaragua earthquake (Mw7.3), which occurred off El Salvador and Nicaragua in Central America. The tsunami numerical simulations were carried out from the determined fault models. We found that the observed tsunami heights, run-up heights, and inundation areas were reasonably well explained by the computed ones. Therefore, our method for tsunami earlywarning purpose should work to estimate a fault model which reproduces tsunami heights near the coast of El Salvador and Nicaragua due to large earthquakes in the subduction zone.

Earthquakeearlywarning systems provide warnings to end users of incoming moderate to strong ground shaking from earthquakes. An earthquakeearlywarning system, ShakeAlert, is providing alerts to beta end users in the western United States, specifically California, Oregon, and Washington. An essential aspect of the earthquakeearlywarning system is the development of a framework to test modifications to code to ensure functionality and assess performance. In 2016, a Testing and Certification Platform (TCP) was included in the development of the Production Prototype version of ShakeAlert. The purpose of the TCP is to evaluate the robustness of candidate code that is proposed for deployment on ShakeAlert Production Prototype servers. TCP consists of two main components: a real‐time in situ test that replicates the real‐time production system and an offline playback system to replay test suites. The real‐time tests of system performance assess code optimization and stability. The offline tests comprise a stress test of candidate code to assess if the code is production ready. The test suite includes over 120 events including local, regional, and teleseismic historic earthquakes, recentering and calibration events, and other anomalous and potentially problematic signals. Two assessments of alert performance are conducted. First, point‐source assessments are undertaken to compare magnitude, epicentral location, and origin time with the Advanced National Seismic System Comprehensive Catalog, as well as to evaluate alert latency. Second, we describe assessment of the quality of ground‐motion predictions at end‐user sites by comparing predicted shaking intensities to ShakeMaps for historic events and implement a threshold‐based approach that assesses how often end users initiate the appropriate action, based on their ground‐shaking threshold. TCP has been developed to be a convenient streamlined procedure for objectively testing algorithms, and it has

Earthquakeearlywarning systems are, in general, designed to be open loop control systems in such a way that the output, i.e., the warning messages, only depend on the input, i.e., recorded ground motions, up to the moment when the message is issued in real-time. We propose an algorithm, which is called Reality Check Algorithm (RCA), which would assess the accuracy of issued warning messages, and then feed the outcome of the assessment back into the system. Then, the system would modify its messages if necessary. That is, we are proposing to convert earthquakeearlywarning systems into feedback control systems by integrating them with RCA. RCA works by continuously monitoring and comparing the observed ground motions' envelopes to the predicted envelopes of Virtual Seismologist (Cua 2005). Accuracy of magnitude and location (both spatial and temporal) estimations of the system are assessed separately by probabilistic classification models, which are trained by a Sparse Bayesian Learning technique called Automatic Relevance Determination prior.

Large earthquakes with long rupture durations emit P wave energy throughout the rupture period. Incorporating late-onset P waves into earthquakeearlywarning (EEW) algorithms could contribute to robust predictions of strong ground motion. Here I describe a technique to detect in real time P waves from growing ruptures to improve the timeliness of an EEW algorithm based on seismic wavefield estimation. The proposed P wave detector, which employs a simple polarization analysis, successfully detected P waves from strong motion generation areas of the 2011 Mw 9.0 Tohoku-oki earthquake rupture. An analysis using 23 large (M ≥ 7) events from Japan confirmed that seismic intensity predictions based on the P wave detector significantly increased lead times without appreciably decreasing the prediction accuracy. P waves from growing ruptures, being one of the fastest carriers of information on ongoing rupture development, have the potential to improve the performance of EEW systems.

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquakeearlywarning (EEW) that uses observed phase arrivals, ground motion amplitudes and selected prior information to estimate earthquake magnitude, location and origin time, and predict the distribution of peak ground motion throughout a region using envelope attenuation relationships. Implementation of the VS algorithm in California is an on-going effort of the Swiss Seismological Service (SED) at ETH Zürich. VS is one of three EEW algorithms - the other two being ElarmS (Allen and Kanamori, 2003) and On-Site (Wu and Kanamori, 2005; Boese et al., 2008) - that form the basis of the California Integrated Seismic Network ShakeAlert system, a prototype end-to-end EEW system that could potentially be implemented in California. The current prototype version of VS in California requires picks at 4 stations to initiate an event declaration. On average, taking into account data latency, variable station distribution, and processing time, this initial estimate is available about 20 seconds after the earthquake origin time, corresponding to a blind zone of about 70 km around the epicenter which would receive no warning, but where it would be the most useful. To increase the available warning time, we want to produce EEW estimates faster (with less than 4 stations). However, working with less than 4 stations with our current approach would increase the number of false alerts, for which there is very little tolerance in a useful EEW system. We explore the use of back-azimuth estimations and the Voronoi-based concept of not-yet-arrived data for reducing false alerts of the earliest VS estimates. The concept of not-yet-arrived data was originally used to provide evolutionary location estimates in EEW (Horiuchi, 2005; Cua and Heaton, 2007; Satriano et al. 2008). However, it can also be applied in discriminating between earthquake and non-earthquake signals. For real earthquakes, the

Earthquakeearlywarning (EEW) is an application of seismological science that can give people, as well as mechanical and electrical systems, up to tens of seconds to take protective actions before peak earthquake shaking arrives at a location. Since 2006, the U.S. Geological Survey has been working in collaboration with several partners to develop EEW for the United States. The goal is to create and operate an EEW system, called ShakeAlert, for the highest risk areas of the United States, starting with the West Coast states of California, Oregon, and Washington. In early 2016, the Production Prototype v.1.0 was established for California; then, in early 2017, v.1.2 was established for the West Coast, with earthquake notifications being distributed to a group of beta users in California, Oregon, and Washington. The new ShakeAlert Production Prototype was an outgrowth from an earlier demonstration EEW system that began sending test notifications to selected users in California in January 2012. ShakeAlert leverages the considerable physical, technical, and organizational earthquake monitoring infrastructure of the Advanced National Seismic System, a nationwide federation of cooperating seismic networks. When fully implemented, the ShakeAlert system may reduce damage and injury caused by large earthquakes, improve the nation’s resilience, and speed recovery.

The ShakeAlert earthquakeearlywarning system for the west coast of the United States is designed to combine information from multiple independent earthquake analysis algorithms in order to provide the public with robust predictions of shaking intensity at each user's location before they are affected by strong shaking. The current contributing analyses come from algorithms that determine the origin time, epicenter, and magnitude of an earthquake (On-site, ElarmS, and Virtual Seismologist). A second generation of algorithms will provide seismic line source information (FinDer), as well as geodetically-constrained slip models (BEFORES, GPSlip, G-larmS, G-FAST). These new algorithms will provide more information about the spatial extent of the earthquake rupture and thus improve the quality of the resulting shaking forecasts.Each of the contributing algorithms exploits different features of the observed seismic and geodetic data, and thus each algorithm may perform differently for different data availability and earthquake source characteristics. Thus the ShakeAlert system requires a central mediator, called the Central Decision Module (CDM). The CDM acts to combine disparate earthquake source information into one unified shaking forecast. Here we will present a new design for the CDM that uses a Bayesian framework to combine earthquake reports from multiple analysis algorithms and compares them to observed shaking information in order to both assess the relative plausibility of each earthquake report and to create an improved unified shaking forecast complete with appropriate uncertainties. We will describe how these probabilistic shaking forecasts can be used to provide each user with a personalized decision-making tool that can help decide whether or not to take a protective action (such as opening fire house doors or stopping trains) based on that user's distance to the earthquake, vulnerability to shaking, false alarm tolerance, and time required to act.

Educated users who have developed response plans and procedures are just as important for an earthquakeearlywarning (EEW) system as are the algorithms and computers that process the data and produce the warnings. In Japan, for example, the implementation of the EEW system which now provides advanced alerts of ground shaking included intense outreach efforts to both institutional and individual recipients. Alerts are now used in automatic control systems that stop trains, place sensitive equipment in safe mode and isolate hazards while the public takes cover. In California, the California Integrated Seismic Network (CISN) is now developing and implementing components of a prototype system for EEW, ShakeAlert. As this processing system is developed, we invite a suite of perspective users from critical industries and institutions throughout California to partner with us in developing useful ShakeAlert products and procedures. At the same time, we will support their efforts to determine and implement appropriate responses to an earlywarning of earthquake shaking. As a first step, in a collaboration with BART, we have developed a basic system allowing BART’s operation center to receive realtime ground shaking information from more than 150 seismic stations operating in the San Francisco Bay Area. BART engineers are implementing a display system for this information. Later phases will include the development of improved response procedures utilizing this information. We plan to continue this collaboration to include more sophisticated information from the prototype CISN ShakeAlert system.

Recently, progress has been made to demonstrate feasibility and benefits of including real-time GPS (rtGPS) in earthquakeearlywarning and rapid response systems. While most concepts have yet to be integrated into operational environments, the Berkeley Seismological Laboratory is currently running an rtGPS based finite fault inversion scheme in true real-time, which is triggered by the seismic-based ShakeAlert system and then sends updated earthquake alerts to a test receiver. The Geodetic Alarm System (G-larmS) was online and responded to the 2014 Mw6.0 South Napa earthquake in California. We review G-larmS' performance during this event and for 13 aftershocks, and we present rtGPS observations and real-time modeling results for the main shock. The first distributed slip model and a magnitude estimate of Mw5.5 were available 24 s after the event origin time, which could be reduced to 14 s after a bug fix (~8 s S-wave travel time, ~6 s data latency). The system continued to re-estimate the magnitude once every second: it increased to Mw5.9 3 s after the first alert and stabilized at Mw5.8 after 15 s. G-larmS' solutions for the subsequent small magnitude aftershocks demonstrate that Mw~6.0 is the current limit for alert updates to contribute back to the seismic-based earlywarning system.

As part of an effort to promote the use of NASA-sponsored Earth science information for disaster risk reduction, real-time high-rate seismogeodetic data are being incorporated into earlywarning and structural monitoring systems. Seismogeodesy combines seismic acceleration and GPS displacement measurements using a tightly-coupled Kalman filter to provide absolute estimates of seismic acceleration, velocity and displacement. Traditionally, the monitoring of earthquakes and tsunamis has been based on seismic networks for estimating earthquake magnitude and slip, and tide gauges and deep-ocean buoys for direct measurement of tsunami waves. Real-time seismogeodetic observations at subduction zones allow for more robust and rapid magnitude and slip estimation that increase warning time in the near-source region. A NASA-funded effort to utilize GPS and seismogeodesy in NOAA's Tsunami Warning Centers in Alaska and Hawaii integrates new modules for picking, locating, and estimating magnitudes and moment tensors for earthquakes into the USGS earthworm environment at the TWCs. In a related project, NASA supports the transition of this research to seismogeodetic tools for disaster preparedness, specifically by implementing GPS and low-cost MEMS accelerometers for structural monitoring in partnership with earthquake engineers. Real-time high-rate seismogeodetic structural monitoring has been implemented on two structures. The first is a parking garage at the Autonomous University of Baja California Faculty of Medicine in Mexicali, not far from the rupture of the 2011 Mw 7.2 El Mayor Cucapah earthquake enabled through a UCMexus collaboration. The second is the 8-story Geisel Library at University of California, San Diego (UCSD). The system has also been installed for several proof-of-concept experiments at the UCSD Network for Earthquake Engineering Simulation (NEES) Large High Performance Outdoor Shake Table. We present MEMS-based seismogeodetic observations from the 10 June

When accompanied by appropriate training and preparedness of a population, EarthquakeEarlyWarning Systems (EEWS) are effective and viable tools for the real-time reduction of societal exposure to seismic events in metropolitan areas. The Italian Accelerometric Network, RAN, which consists of about 500 stations installed over all the active seismic zones, as well as many cities and strategic infrastructures in Italy, has the potential to serve as a nationwide earlywarning system. In this work, we present a feasibility study for a nationwide EEWS in Italy obtained by the integration of the RAN and the software platform PRobabilistic and Evolutionary earlywarning SysTem (PRESTo). The performance of the RAN-PRESTo EEWS is first assessed by testing it on real strong motion recordings of 40 of the largest earthquakes that have occurred during the last 10 years in Italy. Furthermore, we extend the analysis to regions that did not experience earthquakes by considering a nationwide grid of synthetic sources capable of generating Gutenberg-Richter sequences corresponding to the one adopted by the seismic hazard map of the Italian territory. Our results indicate that the RAN-PRESTo EEWS could theoretically provide for higher seismic hazard areas reliable alert messages within about 5 to 10 s and maximum lead times of about 25 s. In case of large events (M > 6.5), this amount of lead time would be sufficient for taking basic protective measures (e.g., duck and cover, move away from windows or equipment) in tens to hundreds of municipalities affected by large ground shaking.

Earthquakeearlywarning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This earlywarning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications. Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake. To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that

The activities of debris flow (DF) in the Wenchuan earthquake-affected area significantly increased after the earthquake on 12 May 2008. The safety of the lives and property of local people is threatened by DFs. A physics-based earlywarning system (EWS) for DF forecasting was developed and applied in this earthquake area. This paper introduces an application of the system in the Wenchuan earthquake-affected area and analyzes the prediction results via a comparison to the DF events triggered by the strong rainfall events reported by the local government. The prediction accuracy and efficiency was first compared with a contribution-factor-based system currently used by the weather bureau of Sichuan province. The storm on 17 August 2012 was used as a case study for this comparison. The comparison shows that the false negative rate and false positive rate of the new system is, respectively, 19 and 21 % lower than the system based on the contribution factors. Consequently, the prediction accuracy is obviously higher than the system based on the contribution factors with a higher operational efficiency. On the invitation of the weather bureau of Sichuan province, the authors upgraded their prediction system of DF by using this new system before the monsoon of Wenchuan earthquake-affected area in 2013. Two prediction cases on 9 July 2013 and 10 July 2014 were chosen to further demonstrate that the new EWS has high stability, efficiency, and prediction accuracy.

Since the Wenchuan earthquake in 2008, a dramatic progress on earthquakeearlywarning (EEW) has been made by Institute of Care-life (ICL) in China. The research on EEW by ICL covers choosing appropriate sensors, methods of installing the sensors, data automatic process methods of the seismic waves for EEW, methods of applying of EEW warnings for public, schools and life-line projects. ICL innovatively applies distributed computing and cloud computing technology. So far, ICL has deployed over 5500 EEW sensors in China, which is 5 times the number of EEW sensors in Japan, covering more than 2.1 million square kilometers. Since June, 2011, over 5000 earthquakes, with 28 of them are destructive quakes, have triggered the EEWS with no false alert. The root mean square (RMS) error of the magnitude for the 28 destructive quakes is 0.32. In addition, innovative work is done to suppress false alarm and miss alarm, which pushes forward the application of EEW in China. The technology is also being applied in Nepal now.

Seismicity in South Korea is low and magnitudes of recent earthquakes are mostly less than 4.0. However, historical earthquakes of South Korea reveal that many damaging earthquakes had occurred in the Korean Peninsula. To mitigate potential seismic hazard in the Korean Peninsula, earthquakeearlywarning (EEW) system is being installed and will be operated in South Korea in the near future. In order to deliver earlywarnings successfully, it is very important to develop stable magnitude scaling relationships. In this study, two empirical magnitude relationships are developed from 350 events ranging in magnitude from 2.0 to 5.0 recorded by the KMA and the KIGAM. 1606 vertical component seismograms whose epicentral distances are within 100 km are chosen. The peak amplitude and the maximum predominant period of the initial P wave are used for finding magnitude relationships. The peak displacement of seismogram recorded at a broadband seismometer shows less scatter than the peak velocity of that. The scatters of the peak displacement and the peak velocity of accelerogram are similar to each other. The peak displacement of seismogram differs from that of accelerogram, which means that two different magnitude relationships for each type of data should be developed. The maximum predominant period of the initial P wave is estimated after using two low-pass filters, 3 Hz and 10 Hz, and 10 Hz low-pass filter yields better estimate than 3 Hz. It is found that most of the peak amplitude and the maximum predominant period are estimated within 1 sec after triggering.

Earthquakes, including large damaging events, are as central to the geologic evolution of the Island of Hawai`i as its more famous volcanic eruptions and lava flows. Increasing and expanding development of facilities and infrastructure on the island continues to increase exposure and risk associated with strong ground shaking resulting from future large local earthquakes. Damaging earthquakes over the last fifty years have shaken the most heavily developed areas and critical infrastructure of the island to levels corresponding to at least Modified Mercalli Intensity VII. Hawai`i's most recent damaging earthquakes, the M6.7 Kiholo Bay and M6.0 Mahukona earthquakes, struck within seven minutes of one another off of the northwest coast of the island in October 2006. These earthquakes resulted in damage at all thirteen of the telescopes near the summit of Mauna Kea that led to gaps in telescope operations ranging from days up to four months. With the experiences of 2006 and Hawai`i's history of damaging earthquakes, we have begun a study to explore the feasibility of implementing earthquakeearlywarning systems to provide advanced warnings to the Thirty Meter Telescope of imminent strong ground shaking from future local earthquakes. One of the major challenges for earthquakeearlywarning in Hawai`i is the variety of earthquake sources, from shallow crustal faults to deeper mantle sources, including the basal decollement separating the volcanic pile from the ancient oceanic crust. Infrastructure on the Island of Hawai`i may only be tens of kilometers from these sources, allowing warning times of only 20 s or less. We assess the capability of the current seismic network to produce alerts for major historic earthquakes, and we will provide recommendations for upgrades to improve performance.

The Sendai Framework for Disaster Risk Reduction 2015-2030 recognizes the need to "substantially increase the availability of and access to multi-hazard earlywarning systems and disaster risk information and assessments to the people by 2030" as one of its global targets (target "g"). While considerable progress has been made in recent decades, earlywarning systems (EWSs) continue to be less developed for geo-hazards and significant challenges remain in advancing the development of EWSs for specific hazards, particularly for fastest onset hazards such as earthquakes. An earthquakeearlywarning system (EEWS) helps in disseminating timely information about potentially catastrophic earthquake hazards to the public, emergency managers and the private sector to provide enough time to implement automatized emergency measures. At the same time, these systems help to reduce considerably the CO2 emissions produced by the catastrophic impacts and subsequent effects of earthquakes, such as those generated by fires, collapses, and pollution (among others), as well as those produced in the recovery and reconstruction processes. In recent years, EEWSs have been developed independently in few countries: EEWSs have shown operational in Japan and Mexico, while other regions in California (USA), Turkey, Italy, Canada, South Korea and China (including Taiwan) are in the development stages or under restricted applications. Many other countries in the Indian Subcontinent, Southeast Asia, Central Asia, Middle East, Eastern Africa, Southeast Africa, as well as Central America, South America and the Caribbean, are located in some of the most seismically active regions in the world, or present moderate seismicity but high vulnerability, and would strongly benefit from the development of EEWSs. Given that, in many instances, the development of an EEWS still requires further testing, increased density coverage in seismic observation stations, regional coordination, and further scientific

State of the art network-based EarthquakeEarlyWarning (EEW) systems can provide warnings for large magnitude 7+ earthquakes. Although regions in the direct vicinity of the epicenter will not receive warnings prior to damaging shaking, real-time event characterization is available before the destructive S-wave arrival across much of the strongly affected region. In contrast, in the case of the more frequent medium size events, such as the devastating 1994 Mw6.7 Northridge, California, earthquake, providing timely warning to the smaller damage zone is more difficult. For such events the "blind zone" of current systems (e.g. the CISN ShakeAlert system in California) is similar in size to the area over which severe damage occurs. We propose a faster and more robust Bayesian inference-based event associator, that in contrast to the current standard associators (e.g. Earthworm Binder), is tailored to EEW and exploits information other than only phase arrival times. In particular, the associator potentially allows for reliable automated event association with as little as two observations, which, compared to the ShakeAlert system, would speed up the real-time characterizations by about ten seconds and thus reduce the blind zone area by up to 80%. We compile an extensive data set of regional and teleseismic earthquake and noise waveforms spanning a wide range of earthquake magnitudes and tectonic regimes. We pass these waveforms through a causal real-time filterbank with passband filters between 0.1 and 50Hz, and, updating every second from the event detection, extract the maximum amplitudes in each frequency band. Using this dataset, we define distributions of amplitude maxima in each passband as a function of epicentral distance and magnitude. For the real-time data, we pass incoming broadband and strong motion waveforms through the same filterbank and extract an evolving set of maximum amplitudes in each passband. We use the maximum amplitude distributions to check

PRESTo (PRobabilistic and Evolutionary earlywarning SysTem) is the software platform for EarthquakeEarlyWarning (EEW) in Southern Italy, that integrates recent algorithms for real-time earthquake location, magnitude estimation and damage assessment, into a highly configurable and easily portable package. The system is under active experimentation based on the Irpinia Seismic Network (ISNet). PRESTo processes the live streams of 3C acceleration data for P-wave arrival detection and, while an event is occurring, promptly performs event detection and provides location, magnitude estimations and peak ground shaking predictions at target sites. The earthquake location is obtained by an evolutionary, real-time probabilistic approach based on an equal differential time formulation. At each time step, it uses information from both triggered and not-yet-triggered stations. Magnitude estimation exploits an empirical relationship that correlates it to the filtered Peak Displacement (Pd), measured over the first 2-4 s of P-signal. Peak ground-motion parameters at any distance can be finally estimated by ground motion prediction equations. Alarm messages containing the updated estimates of these parameters can thus reach target sites before the destructive waves, enabling automatic safety procedures. Using the real-time data streaming from the ISNet network, PRESTo has produced a bulletin for about a hundred low-magnitude events occurred during last two years. Meanwhile, the performances of the EEW system were assessed off-line playing-back the records for moderate and large events from Italy, Spain and Japan and synthetic waveforms for large historical events in Italy. These tests have shown that, when a dense seismic network is deployed in the fault area, PRESTo produces reliable estimates of earthquake location and size within 5-6 s from the event origin time (To). Estimates are provided as probability density functions whose uncertainty typically decreases with time

Here we propose a new method which allows for issuing an earlywarning based upon the real-time mapping of the Potential Damage Zone (PDZ), e.g. the epicentral area where the peak ground velocity is expected to exceed the damaging or strong shaking levels with no assumption about the earthquake rupture extent and spatial variability of ground motion. The system includes the techniques for a refined estimation of the main source parameters (earthquake location and magnitude) and for an accurate prediction of the expected ground shaking level. The system processes the 3-component, real-time ground acceleration and velocity data streams at each station. For stations providing high quality data, the characteristic P-wave period (τc) and the P-wave displacement, velocity and acceleration amplitudes (Pd, Pv and Pa) are jointly measured on a progressively expanded P-wave time window. The evolutionary estimate of these parameters at stations around the source allow to predict the geometry and extent of PDZ, but also of the lower shaking intensity regions at larger epicentral distances. This is done by correlating the measured P-wave amplitude with the Peak Ground Velocity (PGV) and Instrumental Intensity (IMM) and by interpolating the measured and predicted P-wave amplitude at a dense spatial grid, including the nodes of the accelerometer/velocimeter array deployed in the earthquake source area. Depending of the network density and spatial source coverage, this method naturally accounts for effects related to the earthquake rupture extent (e.g. source directivity) and spatial variability of strong ground motion related to crustal wave propagation and site amplification. We have tested this system by a retrospective analysis of three earthquakes: 2016 Italy 6.5 Mw, 2008 Iwate-Miyagi 6.9 Mw and 2011 Tohoku 9.0 Mw. Source parameters characterization are stable and reliable, also the intensity map shows extended source effects consistent with kinematic fracture models of

Over the past several years, USGS has developed the infrastructure for integrating real-time GPS with seismic data in order to improve our ability to respond to earthquakes and volcanic activity. As part of this effort, we have tested real-time GPS processing software components , and identified the most robust and scalable options. Simultaneously, additional near-field monitoring stations have been built using a new station design that combines dual-frequency GPS with high quality strong-motion sensors and dataloggers. Several existing stations have been upgraded in this way, using USGS Multi-Hazards Demonstration Project and American Recovery and Reinvestment Act funds in southern California. In particular, existing seismic stations have been augmented by the addition of GPS and vice versa. The focus of new instrumentation as well as datalogger and telemetry upgrades to date has been along the southern San Andreas fault in hopes of 1) capturing a large and potentially damaging rupture in progress and augmenting inputs to earthquakeearlywarning systems, and 2) recovering high quality recordings on scale of large dynamic displacement waveforms, static displacements and immediate and long-term post-seismic transient deformation. Obtaining definitive records of large ground motions close to a large San Andreas or Cascadia rupture (or volcanic activity) would be a fundamentally important contribution to understanding near-source large ground motions and the physics of earthquakes, including the rupture process and friction associated with crack propagation and healing. Soon, telemetry upgrades will be completed in Cascadia and throughout the Plate Boundary Observatory as well. By collaborating with other groups on open-source automation system development, we will be ready to process the newly available real-time GPS data streams and to fold these data in with existing strong-motion and other seismic data. Data from these same stations will also serve the very

The ElarmS earthquakeearlywarning system has been detecting earthquakes throughout California since 2007. It is one of the algorithms that contributes to the West Coast ShakeAlert, a prototype earthquakeearlywarning system being developed for the US West Coast. ElarmS is also running in the Pacific Northwest, and in Israel, Chile, Turkey, and Peru in test mode. We summarize the performance of the ElarmS system over the past year and review some of the more problematic events that the system has encountered. During the first half of 2016 (2016-01-01 through 2016-07-21), ElarmS successfully alerted on all events with ANSS catalog magnitudes M>3 in the Los Angeles area. The mean alert time for these 9 events was just 4.84 seconds. In the San Francisco Bay Area, ElarmS detected 26 events with ANSS catalog magnitudes M>3. The alert times for these events is 9.12 seconds. The alert times are longer in the Bay Area than in the Los Angeles area due to the sparser network of stations in the Bay Area. 7 Bay Area events were not detected by ElarmS. These events occurred in areas where there is less dense station coverage. In addition, ElarmS sent alerts for 13 of the 16 moderately-sized (ANSS catalog magnitudes M>4) events that occurred throughout the state of California. One of those missed events was a M4.5 that occurred far offshore in the northernmost part of the state. The other two missed events occurred inland in regions with sparse station coverage. Over the past year, we have worked towards the implementation of a new filterbank teleseismic filter algorithm, which we will discuss. Other than teleseismic events, a significant cause of false alerts and severely mislocated events is spurious triggers being associated with triggers from a real earthquake. Here, we address new approaches to filtering out problematic triggers.

The USGS is working with partners to develop the ShakeAlert EarthquakeEarlyWarning (EEW) system (http://pubs.usgs.gov/fs/2014/3083/) to protect life and property along the U.S. West Coast, where the highest national seismic hazard is concentrated. EEW sends an alert that shaking from an earthquake is on its way (in seconds to tens of seconds) to allow recipients or automated systems to take appropriate actions at their location to protect themselves and/or sensitive equipment. ShakeAlert is transitioning toward a production prototype phase in which test users might begin testing applications of the technology. While a subset of uses will be automated (e.g., opening fire house doors), other applications will alert individuals by radio or cellphone notifications and require behavioral decisions to protect themselves (e.g., "Drop, Cover, Hold On"). The project needs to select and move forward with a consistent alert sound to be widely and quickly recognized as an earthquake alert. In this study we combine EEW science and capabilities with an understanding of human behavior from the social and psychological sciences to provide insight toward the design of effective sounds to help best motivate proper action by alert recipients. We present a review of existing research and literature, compiled as considerations and recommendations for alert sound characteristics optimized for EEW. We do not yet address wording of an audible message about the earthquake (e.g., intensity and timing until arrival of shaking or possible actions), although it will be a future component to accompany the sound. We consider pitch(es), loudness, rhythm, tempo, duration, and harmony. Important behavioral responses to sound to take into account include that people respond to discordant sounds with anxiety, can be calmed by harmony and softness, and are innately alerted by loud and abrupt sounds, although levels high enough to be auditory stressors can negatively impact human judgment.

This presentation outlines the EarthquakeEarlyWarning of the Japan Meteorological Agency (JMA) for the 2011 off the Pacific coast of Tohoku Earthquake (Mw9.0). EEW has been operational nationwide in Japan by JMA since October, 2007. For JMA EEW, the hypocenter is determined by a combination of several techniques, using approximately 1,100 stations from the JMA network and the Hi-net network of NIED; magnitude is mainly from maximum displacement amplitudes. JMA EEWs are updated as available data increases with elapsed time. Accordingly EEWs are issued repeatedly with improving accuracy for a single earthquake. JMA EEWs are divided into two grades depending on the expected intensities. The JMA intensity scale is based on instrumental measurements in which not only the amplitude but also the frequency and duration of the shaking are considered. The 10-degree JMA intensity scale rounds off the instrumental intensity value to the integer. Intensities of 5 and 6 are divided into two degrees, namely 5-lower, 5-upper, 6-lower and 6-upper, respectively. Intensity 1 corresponds to ground motion that people can barely detect, and 7 is the upper limit. JMA EEWs are announced to general public when intensity 5-lower (or greater) is expected. The JMA EEW system was triggered for the Mw 9.0 earthquake when station OURI (138km from the epicenter) detected the initial P wave at 14:46:40.2 (Japan Standard Time). The first EEW, the first of 15 announcements, was issued 5.4 s later. The waveform started with small amplitude, which was comparable to noise level for displacement. The small amplitude does not indicate that the initial rupture of the Mw 9.0 event is large, and does not suggest a large magnitude event. By the fourth EEW, 8.6 s after the first trigger, the expected intensity exceeded the criteria of the warning to the general public. JMA issued the fourth EEW announcements to the general public of the Tohoku district, and then the warning was automatically broadcast

Sequence of the 2016 Kumamoto earthquakes (Mw6.2 on April 14, Mw7.0 on April 16, and many aftershocks) caused a devastating damage at Kumamoto and Oita prefectures, Japan. During the Mw7.0 event, just after the direct S waves passing the central Oita, another M6 class event occurred there more than 80 km apart from the Mw7.0 event. The M6 event is interpreted as an induced earthquake; but it brought stronger shaking at the central Oita than that from the Mw7.0 event. We will discuss the induced earthquake from viewpoint of EarthquakeEarlyWarning. In terms of ground shaking such as PGA and PGV, the Mw7.0 event is much smaller than those of the M6 induced earthquake at the central Oita (for example, 1/8 smaller at OIT009 station for PGA), and then it is easy to discriminate two events. However, PGD of the Mw7.0 is larger than that of the induced earthquake, and its appearance is just before the occurrence of the induced earthquake. It is quite difficult to recognize the induced earthquake from displacement waveforms only, because the displacement is strongly contaminated by that of the preceding Mw7.0 event. In many methods of EEW (including current JMA EEW system), magnitude is used for prediction of ground shaking through Ground Motion Prediction Equation (GMPE) and the magnitude is often estimated from displacement. However, displacement magnitude does not necessarily mean the best one for prediction of ground shaking, such as PGA and PGV. In case of the induced earthquake during the Kumamoto earthquake, displacement magnitude could not be estimated because of the strong contamination. Actually JMA EEW system could not recognize the induced earthquake. One of the important lessons we learned from eight years' operation of EEW is an issue of the multiple simultaneous earthquakes, such as aftershocks of the 2011 Mw9.0 Tohoku earthquake. Based on this lesson, we have proposed enhancement of real-time monitor of ground shaking itself instead of rapid estimation of

The Virtual Seismologist (VS) algorithm is a Bayesian approach to regional, network-based earthquakeearlywarning (EEW). Bayes' theorem as applied in the VS algorithm states that the most probable source estimates at any given time is a combination of contributions from relatively static prior information that does not change over the timescale of earthquake rupture and a likelihood function that evolves with time to take into account incoming pick and amplitude observations from the on-going earthquake. Potentially useful types of prior information include network topology or station health status, regional hazard maps, earthquake forecasts, and the Gutenberg-Richter magnitude-frequency relationship. The VS codes provide magnitude and location estimates once picks are available at 4 stations; these source estimates are subsequently updated each second. The algorithm predicts the geographical distribution of peak ground acceleration and velocity using the estimated magnitude and location and appropriate ground motion prediction equations; the peak ground motion estimates are also updated each second. Implementation of the VS algorithm in California and Switzerland is funded by the Seismic EarlyWarning for Europe (SAFER) project. The VS method is one of three EEW algorithms whose real-time performance is being evaluated and tested by the California Integrated Seismic Network (CISN) EEW project. A crucial component of operational EEW algorithms is the ability to distinguish between noise and earthquake-related signals in real-time. We discuss various empirical approaches that allow the VS algorithm to operate in the presence of noise. Real-time operation of the VS codes at the Southern California Seismic Network (SCSN) began in July 2008. On average, the VS algorithm provides initial magnitude, location, origin time, and ground motion distribution estimates within 17 seconds of the earthquake origin time. These initial estimate times are dominated by the time for 4

We present the results of a feasibility study on the application of earthquakeearly-warning procedures in the high school ITIS E. Majorana, Somma Vesuviana, Naples, located about 80 km far from the seismogenic Irpinia region. The study was performed in the framework of the European REAKT project. The school was equipped with an EEWS composed of: a small seismic network of accelerometers, the PRESToPlus software platform, and an actuator, named Sentinel. The Sentinel is made up of low-cost hardware (i.e., Arduino®) programmed to accomplish three main tasks: 1) listen and interpret messages delivered by the EEW system PRESToPlus on the ground motion severity expected at the target site; 2) provides different warnings as alert levels by the control of different hardware (i.e., alarm bells, emergency lights, and so on); 3) declare the end of the most threatening condition, which will assist the emergency coordinator starting the evacuation plan defined by the current legislation. The Sentinel was developed within REAKT in close collaboration with the students and the teachers of the school. The EEW system and the Sentinel were successfully tested during some blind drills performed during normal school activities.

As measures for underprediction for large earthquakes with finite faults and overprediction for multiple simultaneous earthquakes, Hoshiba (2013), Hoshiba and Aoki (2015), and Kodera et al. (2016) proposed earthquakeearlywarning (EEW) methods that directly predict ground motion by computing the wave propagation of observed ground motion. These methods are expected to predict ground motion with a high accuracy even for complicated scenarios because these methods do not need source parameter estimation. On the other hand, there is room for improvement in their rapidity because they predict strong motion prediction mainly based on the observation of S-waves and do not explicitly use P-wave information available before the S-waves. In this research, we propose a real-time P-wave detector to incorporate P-wave information into these wavefield-estimation approaches. P-waves within a few seconds from the P-onsets are commonly used in many existing EEW methods. In addition, we focus on P-waves that may appear in the later part of seismic waves. Kurahashi and Irikura (2013) mentioned that P-waves radiated from strong motion generation areas (SMGAs) were recognizable after S-waves of the initial rupture point in the 2011 off the Pacific coast of Tohoku earthquake (Mw 9.0) (the Tohoku-oki earthquake). Detecting these P-waves would enhance the rapidity of prediction for the peak ground motion generated by SMGAs. We constructed a real-time P-wave detector that uses a polarity analysis. Using acceleration records in boreholes of KiK-net (band-pass filtered around 0.5-10 Hz with site amplification correction), the P-wave detector performed the principal component analysis with a sliding window of 4 s and calculated P-filter values (e.g. Ross and Ben-Zion, 2014). The application to the Tohoku-oki earthquake (Mw 9.0) showed that (1) peaks of P-filter that corresponded to SMGAs appeared in several stations located near SMGAs and (2) real-time seismic intensities (Kunugi et al

In earthquakeearlywarning systems, real-time shake prediction through wave propagation simulation is a promising approach. Compared with traditional methods, it does not suffer from the inaccurate estimation of source parameters. For computation efficiency, wave direction is assumed to propagate on the 2-D surface of the earth in these methods. In fact, since the seismic wave propagates in the 3-D sphere of the earth, the 2-D space modeling of wave direction results in inaccurate wave estimation. In this paper, we propose a 3-D space numerical shake prediction method, which simulates the wave propagation in 3-D space using radiative transfer theory, and incorporate data assimilation technique to estimate the distribution of wave energy. 2011 Tohoku earthquake is studied as an example to show the validity of the proposed model. 2-D space model and 3-D space model are compared in this article, and the prediction results show that numerical shake prediction based on 3-D space model can estimate the real-time ground motion precisely, and overprediction is alleviated when using 3-D space model.

One measure used to assess the performance of EarthquakeEarlyWarning Systems (EEWS) is the delay time between earthquake origin and issued alert. EEWS latency is dependent on a number of sources (e.g. P-wave propagation, digitisation, transmission, receiver processing, triggering, event declaration). Many regional seismic networks use the SEEDlink protocol; however, packet size is fixed to 512-byte miniSEED records, resulting in transmission latencies of >0.5 s. Data packetisation is seen as one of the main sources of delays in EEWS (Brown et al., 2011). Optimising data-logger and telemetry configurations is a cost-effective strategy to improve EEWS alert times (Behr et al., 2015). Digitisers with smaller, selectable packets can result in faster alerts (Sokos et al., 2016). We propose a new seismic protocol for regional seismic networks benefiting low-latency applications such as EEWS. The protocol, based on Güralp's existing GDI-link format is an efficient and flexible method to exchange data between seismic stations and data centers for a range of network configurations. The main principle is to stream data sample-by-sample instead of fixed-length packets to minimise transmission latency. Self-adaptive packetisation with compression maximises available telemetry bandwidth. Highly flexible metadata fields within GDI-link are compatible with existing miniSEED definitions. Data is sent as integers or floats, supporting a wide range of data formats, including discrete parameters such as Pd & τC for on-site earthquakeearlywarning. Other advantages include: streaming station state-of-health information, instrument control, support of backfilling and fail-over strategies during telemetry outages. Based on tests carried out on the Güralp Minimus data-logger, we show our new protocol can reduce transmission latency to as low as 1 ms. The low-latency protocol is currently being implemented with common processing packages. The results of these tests will help to

Current operational EarthquakeEarlyWarning Systems (EEWS) acquire data with networks of single seismic stations, and compute source parameters assuming earthquakes to be point sources. For large events, the point-source assumption leads to an underestimation of magnitude, and the use of single stations leads to large uncertainties in the locations of events outside the network. We propose the use of mini-arrays to improve EEWS. Mini-arrays have the potential to: (a) estimate reliable hypocentral locations by beam forming (FK-analysis) techniques; (b) characterize the rupture dimensions and account for finite-source effects, leading to more reliable estimates for large magnitudes. Previously, the high price of multiple seismometers has made creating arrays cost-prohibitive. However, we propose setting up mini-arrays of a new seismometer based on low-cost (earthquake in September 2010. As the QCN network was so dense, we were able to use small sub-array of up to ten sensors spread along a maximum area of 1.7x2.2 km2 to demonstrate our approach and to solve for the BAZ of two events (Mw4.7 and Mw5.1) with less than ±10° error. We will also present the new 24-bit device details, benchmarks, and real-time measurements.

EarthquakeEarlyWarning (EEW) is a proven use of seismological science that can give people and businesses outside the epicentral area of a large earthquake up to a minute to take protective actions before the most destructive shaking hits them. Since 2006 several organizations have been collaborating to create such a system in the United States. These groups include the US Geological Survey, Caltech, UC Berkeley, the University of Washington, the Southern California Earthquake Center, the Swiss Federal Institute of Technology, Zürich, the California Office of Emergency Services, and the California Geological Survey. A demonstration version of the system, called ShakeAlert, began sending test notifications to selected users in California in January 2012. In August 2012 San Francisco's Bay Area Rapid Transit district began slowing and stopping trains in response to strong ground shaking. The next step in the project is to progress to a production prototype for the west coast. The system is built on top of the considerable technical and organizational earthquake monitoring infrastructure of the Advanced National Seismic System (ANSS). While a fully functional, robust, public EEW system will require significant new investment and development in several major areas, modest progress is being made with current resources. First, high-quality sensors must be installed with sufficient density, particularly near source faults. Where possible, we are upgrading and augmenting the existing ANSS networks on the west coast. Second, data telemetry from those sensors must be engineered for speed and reliability. Next, robust central processing infrastructure is being designed and built. Also, computer algorithms to detect and characterize the evolving earthquake must be further developed and tested. Last year the Gordon and Betty Moore Foundation funded USGS, Caltech, UCB and UW to accelerate R&D efforts. Every available means of distributing alerts must be used to insure the

Scientists at Caltech, UC Berkeley, the Univ. of SoCal, the Univ. of Washington, the US Geological Survey, and ETH Zurich have developed an earthquakeearlywarning (EEW) demonstration system for California and the Pacific Northwest. To quickly determine the earthquake magnitude and location, 'ShakeAlert' currently processes and interprets real-time data-streams from ~400 seismic broadband and strong-motion stations within the California Integrated Seismic Network (CISN). Based on these parameters, the 'UserDisplay' software predicts and displays the arrival and intensity of shaking at a given user site. Real-time ShakeAlert feeds are currently shared with around 160 individuals, companies, and emergency response organizations to educate potential users about EEW and to identify needs and applications of EEW in a future operational warning system. Recently, scientists at the contributing institutions have started to develop algorithms for ShakeAlert that make use of high-rate real-time GPS data to improve the magnitude estimates for large earthquakes (M>6.5) and to determine slip distributions. Knowing the fault slip in (near) real-time is crucial for users relying on or operating distributed systems, such as for power, water or transportation, especially if these networks run close to or across large faults. As shown in an earlier study, slip information is also useful to predict (in a probabilistic sense) how far a fault rupture will propagate, thus enabling more robust probabilistic ground-motion predictions at distant locations. Finally, fault slip information is needed for tsunami warning, such as in the Cascadia subduction-zone. To handle extended fault-ruptures of large earthquakes in real-time, Caltech and USGS Pasadena are currently developing and testing a two-step procedure that combines seismic and geodetic data; in the first step, high-frequency strong-motion amplitudes are used to rapidly classify near-and far-source stations. Then, the location and

In recent years, real-time tsunami inundation forecasting has been developed with the advances of dense seismic monitoring, GPS Earth observation, offshore tsunami observation networks, and high-performance computing infrastructure (Koshimura et al., 2014). Several uncertainties are involved in tsunami inundation modeling and it is believed that tsunami generation model is one of the great uncertain sources. Uncertain tsunami source model has risk to underestimate tsunami height, extent of inundation zone, and damage. Tsunami source inversion using observed seismic, geodetic and tsunami data is the most effective to avoid underestimation of tsunami, but needs to expect more time to acquire the observed data and this limitation makes difficult to terminate real-time tsunami inundation forecasting within sufficient time. Not waiting for the precise tsunami observation information, but from disaster management point of view, we aim to determine the worst tsunami source scenario, for the use of real-time tsunami inundation forecasting and mapping, using the seismic information of EarthquakeEarlyWarning (EEW) that can be obtained immediately after the event triggered. After an earthquake occurs, JMA's EEW estimates magnitude and hypocenter. With the constraints of earthquake magnitude, hypocenter and scaling law, we determine possible multi tsunami source scenarios and start searching the worst one by the superposition of pre-computed tsunami Green's functions, i.e. time series of tsunami height at offshore points corresponding to 2-dimensional Gaussian unit source, e.g. Tsushima et al., 2014. Scenario analysis of our method consists of following 2 steps. (1) Searching the worst scenario range by calculating 90 scenarios with various strike and fault-position. From maximum tsunami height of 90 scenarios, we determine a narrower strike range which causes high tsunami height in the area of concern. (2) Calculating 900 scenarios that have different strike, dip, length

The currently developed and operational EarthquakeEarlywarning, regional systems ground on the assumption of a point-like earthquake source model and 1-D ground motion prediction equations to estimate the earthquake impact. Here we propose a new network-based method which allows for issuing an alert based upon the real-time mapping of the Potential Damage Zone (PDZ), e.g. the epicentral area where the peak ground velocity is expected to exceed the damaging or strong shaking levels with no assumption about the earthquake rupture extent and spatial variability of ground motion. The platform includes the most advanced techniques for a refined estimation of the main source parameters (earthquake location and magnitude) and for an accurate prediction of the expected ground shaking level. The new software platform (QuakeUp) is under development at the Seismological Laboratory (RISSC-Lab) of the Department of Physics at the University of Naples Federico II, in collaboration with the academic spin-off company RISS s.r.l., recently gemmated by the research group. The system processes the 3-component, real-time ground acceleration and velocity data streams at each station. The signal quality is preliminary assessed by checking the signal-to-noise ratio both in acceleration, velocity and displacement and through dedicated filtering algorithms. For stations providing high quality data, the characteristic P-wave period (τ_c) and the P-wave displacement, velocity and acceleration amplitudes (P_d, Pv and P_a) are jointly measured on a progressively expanded P-wave time window. The evolutionary measurements of the early P-wave amplitude and characteristic period at stations around the source allow to predict the geometry and extent of PDZ, but also of the lower shaking intensity regions at larger epicentral distances. This is done by correlating the measured P-wave amplitude with the Peak Ground Velocity (PGV) and Instrumental Intensity (I_MM) and by mapping the measured and

The March 11, 2011 Tohoku earthquake (M 9.0) was the largest earthquake in Japanese history, and was the best recorded subduction-zone earthquakes in the world. In particular, various offshore geophysical observations revealed large horizontal and vertical seafloor movements, and the tsunami was recorded on high-quality, high-sampling gauges. Analysis of such tsunami waveforms shows a temporal and spatial slip distribution during the 2011 Tohoku earthquake. The fault rupture started near the hypocenter and propagated into both deep and shallow parts of the plate interface. Very large, ~25 m, slip off Miyagi on the deep part of plate interface corresponds to an interplate earthquake of M 8.8, the location and size similar to 869 Jogan earthquake model, and was responsible for the large tsunami inundation in Sendai and Ishinomaki plains. Huge slip, more than 50 m, occurred on the shallow part near the trench axis ~3 min after the earthquake origin time. This delayed shallow rupture (M 8.8) was similar to the 1896 "tsunami earthquake," and was responsible for the large tsunami on the northern Sanriku coast, measured at ~100 km north of the largest slip. Thus the Tohoku earthquake can be decomposed into an interplate earthquake and the triggered "tsunami earthquake." The Japan Meteorological Agency issued tsunami warning 3 minutes after the earthquake, and saved many lives. However, their initial estimation of tsunami height was underestimated, because the earthquake magnitude was initially estimated as M 7.9, hence the computed tsunami heights were lower. The JMA attempts to improve the tsunami warning system, including technical developments to estimate the earthquake size in a few minutes by using various and redundant information, to deploy and utilize the offshore tsunami observations, and to issue a warning based on the worst case scenario if a possibility of giant earthquake exists. Predicting a trigger of another large earthquake would still be a challenge

Methods that use storytelling to gather and synthesize data from people can be advantageous in understanding user needs and designing successful communication products. Using a multidisciplinary approach, we research and prioritize user needs for the ShakeAlert EarthquakeEarlyWarning system (http://pubs.usgs.gov/fs/2014/3083/), drawing on best practices from social and behavioral science, risk communication, and human-centered design. We apply quantitative and qualitative human data collection methods including user surveys, interviews, journey maps, personas, and scenarios. Human-centered design methods leverage storytelling (a) in the acquisition of qualitative behavioral data (e.g. with journey mapping), (b) through goal-driven behaviors and needs that are synthesized into a persona as a composite model of the data, and (c) within context scenarios (the story plot or projected circumstances) in which the persona is placed in context to inform the design of relevant and usable products or services. ShakeAlert, operated by the USGS and partners, has transitioned into a production prototype phase in which users are permitted to begin testing pilot implementations to take protective actions in response to an earthquake alert. While a subset of responses will be automated (e.g., opening fire house doors), other applications of the technology will alert individuals by broadcast, public address, or mobile device notifications and require self-protective behavioral decisions (e.g., "Drop, Cover, and Hold On"). To better understand ShakeAlert user decisions and needs, we use human-centered design methods to synthesize aggregated behavioral data into "personas," which model the common behavioral patterns that can be used to guide plans for the ShakeAlert interface, messaging, and training. We present user data, methods, and resulting personas that will inform decisions moving forward to shape ShakeAlert messaging and training that will be most usable by alert recipients.

Earthquakeearlywarning (EEW) systems aim at providing fast and accurate estimates of event parameters or local ground shaking over wide ranges of source dimensions and epicentral distances. The Swiss Seismological Service (SED) has integrated EEW solutions into the SeisComP3 (SC3) professional earthquake monitoring software. VS(SC3) provides fast magnitude estimates for network-based point-sources using conventional triggering and phases association techniques, while FinDer(SC3) matches the evolving patterns of ground motion to track on-going rupture extent, and can provide accurate ground motion predictions for finite fault ruptures. SC3 is widely used, including in Central America, and at INETER in Nicaragua. In 2016, SED and INETER started a joint project to assess the feasibility of EEW in Nicaragua and Central America and to set up a prototype EEW system. We test VS(SC3) and FinDer(SC3) softwares at INETER since 2016. Excellent relations between regional seismic networks mean broadband and strong motion seismic data are exchanged across Central America in real time, which means the network is sufficient to warrant investigation into its potential for EEW. We report on the successes and challenges of operating an EEW system where seismicity is high, but infrastructure is fragile and the design and operation of a seismic network is challenging (in Nicaragua, on average 50% of all stations do not work effectively for EEW). The current best EEW delays for on-shore earthquakes in Nicaragua is in the order of 20s and 40s offshore. However, the current network should be able to provide EEW in 10 to 15s on-shore and 20 to 25s off-shore which correspond to potential EEW intensities over or equal to VII. We compare the performances of EEW in Nicaragua with an ideal setting, featuring optimized data availability. We evaluate improvements strategies of the Nicaraguan and the Joint Central American Seismic Networks for EEW. And we discuss how to combine real-time EEW

EEW(EarthquakeEarlyWarning) service to the public has been officially operated by KMA (Korea Meteorological Administration) from 2015 in Korea. For the KMA's official EEW service, KIGAM has adopted ElarmS from UC Berkeley BSL and modified local magnitude relation, 1-D travel time curves and association procedures with real time waveform from about 201 seismic stations of KMA, KIGAM, KINS and KEPRI. There were two moderate size earthquakes with magnitude Ml 5.1 and Ml 5.8 close to Gyeongju city located at the southeastern part of Korea on Sep. 12. 2016. We have checked the performance of EEWS(EarthquakeEarlyWarning System) named as TrigDB by KIGAM reviewing of these two Gyeongju earthquakes. The nearest station to epicenters of two earthquakes Ml 5.1(35.7697 N, 129.1904 E) and Ml 5.8(35.7632 N, 129.1898 E) was MKL which detected P phases in about 2.1 and 3.6 seconds after the origin times respectively. The first events were issued in 6.3 and 7.0 seconds from each origin time. Because of the unstable results on the early steps due to very few stations and unexpected automated analysis, KMA has the policy to wait for more 20 seconds for confirming the reliability. For these events KMA published EEW alarms in about 26 seconds after origin times with M 5.3 and M 5.9 respectively.

Ocean Networks Canada (ONC) operates ocean and coastal observatories on all three of Canada's coasts, and more particularly across the Cascadia subduction zone. The data are acquired, parsed, calibrated and archived by ONC's data management system (Oceans 2.0), with real-time event detection, reaction and access capabilities. As such, ONC is in a unique position to develop earlywarning systems for earthquakes, near- and far-field tsunamis and other events. ONC is leading the development of a system to alert southwestern British Columbia of an impending Cascadia subduction zone earthquake on behalf of the provincial government and with the support of the Canadian Federal Government. Similarly to other earlyearthquakewarning systems, an array of accelerometers is used to detect the initial earthquake p-waves. This can provide 5-60 seconds of warning to subscribers who can then take action, such as stopping trains and surgeries, closing valves, taking cover, etc. To maximize the detection capability and the time available to react to a notification, instruments are placed both underwater and on land on Vancouver Island. A novel feature of ONC's system is, for land-based sites, the combination of real-time satellite positioning (GNSS) and accelerometer data in the calculations to improve earthquake intensity estimates. This results in higher accuracy, dynamic range and responsiveness than either type of sensor is capable of alone. P-wave detections and displacement data are sent from remote stations to a data centre that must calculate epicentre locations and magnitude. The latter are then delivered to subscribers with client software that, given their position, will calculate arrival time and intensity. All of this must occur with very high standards for latency, reliability and accuracy.

The Istanbul EEW network consisting of 10 inland and 5 OBS strong motion stations located close to the Main Marmara Fault zone is operated by KOERI. Data transmission between the remote stations and the base station at KOERI is provided both with satellite and fiber optic cable systems. The continuous on-line data from these stations is used to provide real time warning for emerging potentially disastrous earthquakes. The data transmission time from the remote stations to the KOERI data center is a few milliseconds through fiber optic lines and less than a second via satellites. The earlywarning signal (consisting three alarm levels) is communicated to the appropriate servo shut-down systems of the receipent facilities, that automatically decide proper action based on the alarm level. Istanbul Gas Distribution Corporation (IGDAS) is one of the end users of the EEW signal. IGDAS, the primary natural gas provider in Istanbul, operates an extensive system 9,867 km of gas lines with 550 district regulators and 474,000 service boxes. State of-the-art protection systems automatically cut natural gas flow when breaks in the pipelines are detected. Since 2005, buildings in Istanbul using natural gas are required to install seismometers that automatically cut natural gas flow when certain thresholds are exceeded. IGDAS uses a sophisticated SCADA (supervisory control and data acquisition) system to monitor the state-of-health of its pipeline network. This system provides real-time information about quantities related to pipeline monitoring, including input-output pressure, drawing information, positions of station and RTU (remote terminal unit) gates, slum shut mechanism status at 581 district regulator sites. The SCADA system of IGDAŞ receives the EEW signal from KOERI and decide the proper actions according to the previously specified ground acceleration levels. Presently, KOERI sends EEW signal to the SCADA system of IGDAS Natural Gas Network of Istanbul. The EEW signal

As a first step in establishing an earthquakeearlywarning system in Cascadia, we have installed the ElarmS component of the ShakeAlert system at the Pacific Northwest Seismic Network. In Cascadia our initial focus is primarily on the development of a seismo-geodetic-based real-time finite fault rupture algorithm to detect and characterize a large plate-boundary rupture in progress (see Crowell et. al., this session). In this regard the goal of the purely seismic-data-based ElarmS implementation is to 'trigger' the finite fault rupture algorithm. At the same time, however, the Cascadian ElarmS will also produce warnings for smaller onshore crustal earthquakes. While warnings from these smaller and closer earthquakes will provide shorter warning times for communities, and for less dramatic earthquakes, we intend to use them for educational purposes, and to coordinate with our regional and collaborating partners. They will also help to guide us to shorten data latencies and learn where additional instrumentation is most needed to increase performance. The accuracy of ElarmS in Cascadia is another major concern, because the current ElarmS model presumes an initial focal depth for earthquakes of 8 km based on California experience, while in Cascadia earthquakes of major concern may be as deep as 50 km, and/or occur beyond the western fringe of the seismic network. To this purpose our testing protocol is aimed at determining what changes are required to ensure top performance of an ElarmS-based warning system in Cascadia. Because of Cascadia's relatively low seismicity rate, and the paucity of data from plate boundary earthquakes there of any size, we have prioritized the development of a test system. The test system permits us to: 1) replay segments of actual seismic waveform data recorded from the PNSN and contributing seismic network stations to represent both earthquakes and noise conditions, and 2) broadcast synthetic data into the system to simulate signals we

Earthquakeearlywarning of JMA is to enable advance countermeasures to the strong motion disaster by providing expected seismic intensity and arrival time of the strong motion, as well as estimated hypocenter parameters, before the S wave arrival. However, due to its very short available time period, it is essential to well publicize the principle and technical limit of EEW, and proper actions to be taken when it is seen or heard, to utilize EEW effectively without causing unnecessary confusion. Accordingly, JMA decided to provide EEW in two steps. Namely, JMA started to provide EEW to a limited number of users who understand the technical limit of EEW and can utilize it effectively, such as for automatic control from August 2006. At that moment, EEW was not well known to the general public, so JMA started to provide it to the general public in October 2007, after publicizing the principle and proper actions to be taken. EEWs are issued basically several times for one earthquake improving the accuracy as available data increases as time passes, securing the promptness of the first issuance at the same time. On line connected computer can utilize such multiply issued information for automatic control. But, when they are transmitted to a public, it is impossible to respond properly, and also it is impossible to transmit all by characters and voice. So, JMA considered the issuance criterion and contents of EEW when it is issued to the general public to meet the following conditions. 1) Should be issued on the best timing, to avoid the false alarm, to secure the promptness as much as possible, and to make the revised issuance as few as possible. 2) Should be issued when really a strong motion is expected, and it should be made clear where the safety actions should be taken. As a result, - Issuance criterion : when the maximum seismic intensity 5 lower(JMA scale) or over is expected by using seismic records from more than one station. - EEW contents : Origin time

One of the main objective of the WP7 (Strategic Applications and Capacity Building) in the framework of the REAKT-Strategies and tools for Real Time Earthquake RisK ReducTion FP7 European project, is to evaluate the effectiveness of EEW and real-time risk assessment procedures in reducing seismic risk to various industrial partners and end-users. In the context of the REAKT project, the AMRA-RISSCLab group is engaged in a feasibility study on the application of earthquakeearly-warning procedures in two high schools located in the Irpinia region (South Italy), an area that in the 1980 was struck by a magnitude 6.9 earthquake. In this work we report on the activities carried out during the last 24 Months at the school ITIS 'E. Majorana', located in Somma Vesuviana, a village in the neighbourhood of Naples. In order to perform a continuous seismic monitoring of the site, which includes a rather complex structure building, 5 accelerometric stations have been installed in different part of the school. In particular, a 24-bit ADC (Sigma/Delta) Agecodagis-Kefren data-logger has been installed with a Guralp CMG-5TC accelerometer with a 0.25g full-scale in the school courtyard, while 4 SOSEWIN sensors have been also installed at different locations within the building. Commercial ADSL lines provide transmission of real-time data to the EEW centre. Data streams are now acquired in real-time in the PRESToPlus (regional and on-site, threshold-based early-warning) software platform [1]. The recent December 29, 2013 M 5.1 Monti del Matese Earthquake, gave us the unique opportunity to use real strong motion data to test the performance of threshold-based earlywarning method at the school. The on-site method [2] aims to define alert levels at the monitored site. In particular, at each station the characteristic P-waves period (τc) and the peak displacement (Pd) are measured on the initial P-wave signal. They are compared with threshold values, previously established through an

Real-time applications such as earthquakeearlywarning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M > 6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (<0.5 Hz) and broad-band (0–10 Hz) data sets. CyberShake encompasses 3-D wave-propagation simulations of >415 000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20 per cent) of CyberShake simulations, but can explain MMI values of all >400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking

EarthquakeEarlyWarning (EEW) systems can provide as much as tens of seconds of warning to people and automated systems before strong shaking arrives. The United States Geological Survey (USGS) and its partners are developing an EEW system for the West Coast of the United States. To be an integral part of successful implementation, EEW engagement programs and materials must integrate with and leverage broader earthquake risk programs. New methods and products for dissemination must be multidisciplinary, cost effective, and consistent with existing hazards education efforts. Our presentation outlines how the USGS and its partners will approach this effort in the context of the EEW system through the work of a multistate and multiagency committee that participates in the design, implementation, and evaluation of a portfolio of programs and products. This committee, referred to as the ShakeAlert Joint Committee for Communication, Education, and Outreach (ShakeAlert CEO), is working to identify, develop, and cultivate partnerships with EEW stakeholders including Federal, State, academic partners, private companies, policy makers, and local organizations. Efforts include developing materials, methods for delivery, and reaching stakeholders with information on EEW, earthquake preparedness, and emergency protective actions. It is essential to develop standards to ensure information communicated via the EEW alerts is consistent across the public and private sector and achieving a common understanding of what actions users take when they receive an EEW warning. The USGS and the participating states and agencies acknowledge that the implementation of EEW is a collective effort requiring the participation of hundreds of stakeholders committed to ensuring public accessibility.

The Cascadia subduction zone hosts catastrophic earthquakes every few hundred years. On land, there are extensive geophysical networks available to monitor the subduction zone, but since the locked portion of the plate boundary lies mostly offshore, these networks are ideally complemented by seafloor observations. Such considerations helped motivate the development of scientific cabled observatories that cross the subduction zone at two sites off Vancouver Island and one off central Oregon, but these have a limited spatial footprint along the strike of the subduction zone. The Pacific Northwest Seismic Network is leading a collaborative effort to implement an earthquakeearlywarning system in the Washington and Oregon using data streams from land networks as well as the few existing offshore instruments. For subduction zone earthquakes that initiate offshore, this system will provide a warning. However, the availability of real time offshore instrumentation along the entire subduction zone would improve its reliability and accuracy, add up to 15 s to the warning time, and ensure an earlywarning for coastal communities near the epicenter. Furthermore, real-time networks of seafloor pressure sensors above the subduction zone would enable monitoring and contribute to accurate predictions of the incoming tsunami. There is also strong scientific motivation for offshore monitoring. We lack a complete knowledge of the plate convergence rate and direction. Measurements of steady deformation and observations of transient processes such as fluid pulsing, microseismic cycles, tremor and slow-slip are necessary for assessing the dimensions of the locked zone and its along-strike segmentation. Long-term monitoring will also provide baseline observations that can be used to detect and evaluate changes in the subduction environment. There are significant engineering challenges to be solved to ensure the system is sufficiently reliable and maintainable. It must provide

Detection of ionospheric anomalies following the Sumatra and Tohoku earthquakes (e.g., Occhipinti 2015) demonstrated that ionosphere is sensitive to earthquake and tsunami propagation: ground and oceanic vertical displacement induces acoustic-gravity waves propagating within the neutral atmosphere and detectable in the ionosphere. Observations supported by modelling proved that ionospheric anomalies related to tsunamis are deterministic and reproducible by numerical modeling via the ocean/neutral-atmosphere/ionosphere coupling mechanism (Occhipinti et al., 2008). To prove that the tsunami signature in the ionosphere is routinely detected we show here perturbations of total electron content (TEC) measured by GPS and following tsunamigenic earthquakes from 2004 to 2011 (Rolland et al. 2010, Occhipinti et al., 2013), nominally, Sumatra (26 December, 2004 and 12 September, 2007), Chile (14 November, 2007), Samoa (29 September, 2009) and the recent Tohoku-Oki (11 Mars, 2011). Based on the observations close to the epicenter, mainly performed by GPS networks located in Sumatra, Chile and Japan, we highlight the TEC perturbation observed within the first 8 min after the seismic rupture. This perturbation contains information about the ground displacement, as well as the consequent sea surface displacement resulting in the tsunami. In addition to GNSS-TEC observations close to the epicenter, new exciting measurements in the far-field were performed by airglow measurement in Hawaii show the propagation of the internal gravity waves induced by the Tohoku tsunami (Occhipinti et al., 2011). This revolutionary imaging technique is today supported by two new observations of moderate tsunamis: Queen Charlotte (M: 7.7, 27 October, 2013) and Chile (M: 8.2, 16 September 2015). We finally detail here our recent work (Manta et al., 2017) on the case of tsunami alert failure following the Mw7.8 Mentawai event (25 October, 2010), and its twin tsunami alert response following the Mw7

We report on a prototype earthquakeearlywarning system for the Western U.S. based on GNSS (GPS+GLONASS) observations, and where available collocated GNSS and accelerometer data (seismogeodesy). We estimate with latency of 2-3 seconds GNSS displacement waveforms from more than 120 stations, focusing on the southern segment of the San Andreas fault, the Hayward and Rodgers Creek faults and Cascadia. The displacements are estimated using precise point positioning with ambiguity resolution (PPP-AR), which provides for efficient processing of hundreds of "clients" within the region of interest with respect to a reference frame well outside the expected zone of deformation. The GNSS displacements are useful for alleviating magnitude saturation concerns, rapid earthquake magnitude estimation using peak ground displacements, CMT solutions and finite fault slip models. However, GNSS alone is insufficient for strict earthquakeearlywarning (i.e., P wave detection). Therefore, we employ a self-contained seismogeodetic technique, where collocations of GNSS and accelerometer instruments are available, to estimate real-time displacement and velocity waveforms using PPP-AR with accelerometers (PPP-ARA). Using the velocity waveforms we can detect the P wave arrival for earthquakes of interest (>M 5.5), estimate a hypocenter, S wave propagation, and earthquake magnitude using Pd scaling relationships within seconds. Currently we are gearing up to receive observatory-grade accelerometer data from the CISN. We have deployed 25 inexpensive MEMS accelerometers at existing GNSS stations. The SIO Geodetic Modules that control the flow of the GNSS and accelerometer data are being upgraded with in situ PPP-ARA and P wave picking. In situ processing allows us to use the data at the highest sampling rate of the GNSS receiver (10 Hz or higher), in combination with the 100 Hz accelerometer data. Adding the GLONASS data allows for increased precision in the vertical, an important factor in P

Earthquake parameter estimations using nearest neighbor searching among a large database of observations can lead to reliable prediction results. However, in the real-time application of EarthquakeEarlyWarning (EEW) systems, the accurate prediction using a large database is penalized by a significant delay in the processing time. We propose to use a multidimensional binary search tree (KD tree) data structure to organize large seismic databases to reduce the processing time in nearest neighbor search for predictions. We evaluated the performance of KD tree on the Gutenberg Algorithm, a database-searching algorithm for EEW. We constructed an offline test to predict peak ground motions using a database with feature sets of waveform filter-bank characteristics, and compare the results with the observed seismic parameters. We concluded that large database provides more accurate predictions of the ground motion information, such as peak ground acceleration, velocity, and displacement (PGA, PGV, PGD), than source parameters, such as hypocenter distance. Application of the KD tree search to organize the database reduced the average searching process by 85% time cost of the exhaustive method, allowing the method to be feasible for real-time implementation. The algorithm is straightforward and the results will reduce the overall time of warning delivery for EEW.

EarthquakeEarlyWarning System (EEWS) has shown its efficiency for earthquake damage mitigation. As the progress of low-cost Micro Electro Mechanical System (MEMS), many types of MEMS-based accelerometers have been developed and widely used in deploying large-scale, dense seismic networks for EEWS. However, the noise performance of these commercially available MEMS is still insufficient for weak seismic signals, leading to the large scatter of early-warning parameters estimation. In this study, we developed a new type of tri-axial accelerometer based on high dynamic range MEMS with low noise level using for EEWS. It is a MEMS-integrated data logger with built-in seismological processing. The device is built on a custom-tailored Linux 2.6.27 operating system and the method for automatic detecting seismic events is STA/LTA algorithms. When a seismic event is detected, peak ground parameters of all data components will be calculated at an interval of 1 s, and τc-Pd values will be evaluated using the initial 3 s of P wave. These values will then be organized as a trigger packet actively sent to the processing center for event combining detection. The output data of all three components are calibrated to sensitivity 500 counts/cm/s2. Several tests and a real field test deployment were performed to obtain the performances of this device. The results show that the dynamic range can reach 98 dB for the vertical component and 99 dB for the horizontal components, and majority of bias temperature coefficients are lower than 200 μg/°C. In addition, the results of event detection and real field deployment have shown its capabilities for EEWS and rapid intensity reporting.

Earthquakeearlywarning systems (EEWS) are systems nowadays contributing to the seismic risk mitigation actions, both in terms of losses and societal resilience, by issuing an alert promptly after the earthquake origin and before the ground shaking impacts the targets to be protected. EEWS systems can be grouped in two main classes: network based and stand-alone systems. Network based EEWS make use of dense seismic networks surrounding the fault (e.g. Near Fault Observatory; NFO) generating the event. The rapid processing of the P-wave early portion allows for the location and magnitude estimation of the event then used to predict the shaking through ground motion prediction equations. Stand-alone systems instead analyze the early P-wave signal to predict the ground shaking carried by the late S or surface waves, through empirically calibrated scaling relationships, at the recording site itself. We compared the network-based (PRESTo, PRobabilistic and Evolutionary earlywarning SysTem, www.prestoews.org, Satriano et al., 2011) and the stand-alone (SAVE, on-Site-Alert-leVEl, Caruso et al., 2017) systems, by analyzing their performance during the 2016-2017 Central Italy sequence. We analyzed 9 earthquakes having magnitude 5.0 < M < 6.5 at about 200 stations located within 200 km from the epicentral area, including stations of The Altotiberina NFO (TABOO). Performances are evaluated in terms of rate of success of ground shaking intensity prediction and available lead-time, i.e. the time available for security actions. PRESTo also evaluated the accuracy of location and magnitude. Both systems well predict the ground shaking nearby the event source, with a success rate around 90% within the potential damage zone. The lead-time is significantly larger for the network based system, increasing to more than 10s at 40 km from the event epicentre. The stand-alone system better performs in the near-source region showing a positive albeit small lead-time (<3s). Far away from

integration within the American preparedness culture. Perhaps most importantly, the implementation of ShakeAlert will help prepare the people , businesses...disasters through the use of an earthquakewarning system. In general, people have an expectation that authorities will protect society from natural... Society of America, potentially damaging earthquakes may threaten more than 143 million Americans in the next 50 years, and 28 million persons are

This collaborative research to operations demonstration brings together the data and algorithms from NASA research, technology, and applications-funded projects to deliver relevant data streams, algorithms, predictive models, and visualization tools to the NOAA National Tsunami Warning Center (NTWC) and Pacific Tsunami Warning Center (PTWC). Using real-time GNSS data and models in an operational environment, we will test and evaluate an augmented capability for tsunami earlywarning. Each of three research groups collect data from a selected network of real-time GNSS stations, exchange data consisting of independently processed 1 Hz station displacements, and merge the output into a single, more accurate and reliable set. The resulting merged data stream is delivered from three redundant locations to the TWCs with a latency of 5-10 seconds. Data from a number of seismogeodetic stations with collocated GPS and accelerometer instruments are processed for displacements and seismic velocities and also delivered. Algorithms for locating and determining the magnitude of earthquakes as well as algorithms that compute the source function of a potential tsunami using this new data stream are included in the demonstration. The delivered data, algorithms, models and tools are hosted on NOAA-operated machines at both warning centers, and, once tested, the results will be evaluated for utility in improving the speed and accuracy of tsunami warnings. This collaboration has the potential to dramatically improve the speed and accuracy of the TWCs local tsunami information over the current seismometer-only based methods. In our first year of this work, we have established and deployed an architecture for data movement and algorithm installation at the TWC's. We are addressing data quality issues and porting algorithms into the TWCs operating environment. Our initial module deliveries will focus on estimating moment magnitude (Mw) from Peak Ground Displacement (PGD), within 2

This work deals with a methodology applied to seismic earlywarning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source-receiver distance of up to 150 km during the period 1998-2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.

The Virtual Seismologist (VS) algorithm is a Bayesian approach to earthquakeearlywarning (EEW) being implemented by the Swiss Seismological Service at ETH Zurich. The application of Bayes’ theorem in earthquakeearlywarning states that the most probable source estimate at any given time is a combination of contributions from a likelihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS algorithm was one of three EEW algorithms involved in the California Integrated Seismic Network (CISN) real-time EEW testing and performance evaluation effort. Its compelling real-time performance in California over the last three years has led to its inclusion in the new USGS-funded effort to develop key components of CISN ShakeAlert, a prototype EEW system that could potentially be implemented in California. A significant portion of VS code development was supported by the SAFER EEW project in Europe. We discuss recent enhancements to the VS EEW algorithm. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to be declared an event to reduce false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and it requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) to a hybrid on-site/regional approach capable of providing a continuously evolving stream of EEW

EEW(EarthquakeEarlyWarning) service to the public has been officially operated by KMA (Korea Meteorological Administration) from 2015 in Korea. For the KMA's official EEW service, KIGAM has adopted ElarmS from UC Berkeley BSL and modified local magnitude relation, 1-D travel time curves and association procedures with real time waveforms from about 160 seismic stations of KMA and KIGAM. We have checked the performance of EEWS(EarthquakeEarlyWarning System) reviewing two moderate size earthquakes: one is Iksan Eq.(Ml4.3) inside of networks and the other is Ulsan Eq.(Ml5.0) happened at the southern east sea of Korea outside of networks. The first trigger time at NPR station of the Iksan Eq. took 2.3 sec and BUY and JEO2 stations were associated to produce the first event version in 10.07 sec from the origin time respectively. Because the epicentral distance of JEO2 station is about 30 km and the estimated travel time is 6.2 sec, the delay time including transmission and processing is estimated as 3.87 sec with assumption that P wave velocity is 5 km/sec and the focal depth is 8 km. The first magnitude was M4.9 which was a little bigger than Ml4.3 by KIGAM. After adding 3 more triggers of stations (CHO, KMSA, PORA), the estimated magnitude became to M4.6 and the final was settled down to M4.3 with 10 stations. In the case of Ulsan the first trigger time took 11.04 sec and the first alert time with 3 stations in 14.8 sec from the origin time (OT) respectively. The first magnitude was M5.2, however, the difference between the first EEW epicenter and the manual final result was about 63 km due to the poor azimuth coverage outside of seismic network. After 16.2 sec from OT the fourth station YSB was used to update the location near to the manual results within 6 km with magnitude 5.0 and location and magnitude were stable with more stations. Ulsan Eq. was the first case announced to the public by EEWS and the process and result were successful, however, we have to

Earthquake scientists are without doubt experts in understanding earthquake probabilities, magnitudes, and intensities, as well as the potential consequences of them to community infrastructures and inhabitants. One critical challenge these scientific experts face, however, rests with communicating what they know to the people they want to help. Helping scientists translate scientific information to non-scientists is something Drs. Tim and Deanna Sellnow have been committed to for decades. As such, they have compiled a host of data-driven best practices for communicating effectively to non-scientific publics about earthquake forecasting, probabilities, and warnings. In this session, they will summarize what they have learned as it may help earthquake scientists, emergency managers, and other key spokespersons share these important messages to disparate publics in ways that result in positive outcomes, the most important of which is saving lives.

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquakeearlywarning (EEW) originally formulated by Cua and Heaton (2007). Implementation of VS into real-time EEW codes has been an on-going effort of the Swiss Seismological Service at ETH Zürich since 2006, with support from ETH Zürich, various European projects, and the United States Geological Survey (USGS). VS is one of three EEW algorithms that form the basis of the California Integrated Seismic Network (CISN) ShakeAlert system, a USGS-funded prototype end-to-end EEW system that could potentially be implemented in California. In Europe, VS is currently operating as a real-time test system in Switzerland. As part of the on-going EU project REAKT (Strategies and Tools for Real-Time Earthquake Risk Reduction), VS installations in southern Italy, western Greece, Istanbul, Romania, and Iceland are planned or underway. In Switzerland, VS has been running in real-time on stations monitored by the Swiss Seismological Service (including stations from Austria, France, Germany, and Italy) since 2010. While originally based on the Earthworm system it has recently been ported to the SeisComp3 system. Besides taking advantage of SeisComp3's picking and phase association capabilities it greatly simplifies the potential installation of VS at networks in particular those already running SeisComp3. We present the architecture of the new SeisComp3 based version and compare its results from off-line tests with the real-time performance of VS in Switzerland over the past two years. We further show that the empirical relationships used by VS to estimate magnitudes and ground motion, originally derived from southern California data, perform well in Switzerland.

In support of hazard research and EarthquakeEarlyWarning (EEW) Systems UNAVCO operates approximately 800 RT-GNSS stations throughout western North America and Alaska (EarthScope Plate Boundary Observatory), Mexico (TLALOCNet), and the pan-Caribbean region (COCONet). Our system produces and distributes raw data (BINEX and RTCM3) and real-time Precise Point Positions via the Trimble PIVOT Platform (RTX). The 2017-09-08 earthquake M8.2 located 98 km SSW of Tres Picos, Mexico is the first great earthquake to occur within the UNAVCO RT-GNSS footprint, which allows for a rigorous analysis of our dynamic and static processing methods. The need for rapid geodetic solutions ranges from seconds (EEW systems) to several minutes (Tsunami Warning and NEIC moment tensor and finite fault models). Here, we compare and quantify the relative processing strategies for producing static offsets, moment tensors and geodetically determined finite fault models using data recorded during this event. We also compare the geodetic solutions with the USGS NEIC seismically derived moment tensors and finite fault models, including displacement waveforms generated from these models. We define kinematic post-processed solutions from GIPSY-OASISII (v6.4) with final orbits and clocks as a "best" case reference to evaluate the performance of our different processing strategies. We find that static displacements of a few centimeters or less are difficult to resolve in the real-time GNSS position estimates. The standard daily 24-hour solutions provide the highest-quality data-set to determine coseismic offsets, but these solutions are delayed by at least 48 hours after the event. Dynamic displacements, estimated in real-time, however, show reasonable agreement with final, post-processed position estimates, and while individual position estimates have large errors, the real-time solutions offer an excellent operational option for EEW systems, including the use of estimated peak-ground displacements or

A prototype earlywarning system to provide San Francisco and Oakland, California a few tens-of-seconds warning of incoming strong ground shaking from already-occurred M ≧ 3.7 aftershocks of the magnitude 7.1 17 October 1989 Loma Prieta earthquake was operational on 28 October 1989. The prototype system consisted of four components: ground motion sensors in the epicentral area, a central receiver, a radio repeater, and radio receivers. One of the radio receivers was deployed at the California Department of Transportation (CALTRANS) headquarters at the damaged Cypress Street section of the I-880 freeway in Oakland, California on 28 October 1989 and provided about 20 sec of warning before shaking from the M 4.5 Loma Prieta aftershock that occurred on 2 November 1989 at 0550 UTC. In its first 6 months of operation, the system generated triggers for all 12 M > 3.7 aftershocks for which trigger documentation is preserved, did not trigger on any M ≦ 3.6 aftershocks, and produced one false trigger as a result of a now-corrected single point of failure design flaw. Because the prototype system demonstrated that potentially useful warnings of strong shaking from aftershocks are feasible, the USGS has completed a portable earlywarning system for aftershocks that can be deployed anywhere.

The United States Geological Survey (USGS) and its partners are developing the ShakeAlert EarthquakeEarlyWarning System for the West Coast of the United States. To be an integral part of successful implementation, ShakeAlert engagement programs and materials must integrate with and leverage broader earthquake risk programs. New methods and products for dissemination must be multidisciplinary, cost effective, and consistent with existing hazards education and communication efforts. The ShakeAlert Joint Committee for Communication, Education, and Outreach (JCCEO), is identifying, developing, and cultivating partnerships with ShakeAlert stakeholders including Federal, State, academic partners, private companies, policy makers, and local organizations. Efforts include developing materials, methods for delivery, and reaching stakeholders with information on ShakeAlert, earthquake preparedness, and emergency protective actions. It is essential to develop standards to ensure information communicated via the alerts is consistent across the public and private sector and achieving a common understanding of what actions users take when they receive a ShakeAlert warning. In February 2017, the JCCEO convened the Warning Message Focus Group (WMFG) to provide findings and recommendations to the Alliance for Telecommunications Industry Solutions on the use of earthquakeearlywarning message content standards for public alerts via cell phones. The WMFG represents communications, education, and outreach stakeholders from various sectors including ShakeAlert regional coordinators, industry, emergency managers, and subject matter experts from the social sciences. The group knowledge was combined with an in-depth literature review to ensure that all groups who could receive the message would be taken into account. The USGS and the participating states and agencies acknowledge that the implementation of ShakeAlert is a collective effort requiring the participation of hundreds of

EarthquakeEarlyWarning (EEW) systems can provide as much as tens of seconds of warning to people and automated systems before strong shaking arrives. The United States Geological Survey (USGS) and its partners are developing such an EEW system, called ShakeAlert, for the West Coast of the United States. This document describes the technical implementation of that system, which leverages existing stations and infrastructure of the Advanced National Seismic System (ANSS) regional networks to achieve this new capability. While significant progress has been made in developing the ShakeAlert earlywarning system, improved robustness of each component of the system and additional testing and certification are needed for the system to be reliable enough to issue public alerts. Major components of the system include dense networks of ground motion sensors, telecommunications from those sensors to central processing systems, algorithms for event detection and alert creation, and distribution systems to alert users. Capital investment costs for a West Coast EEW system are projected to be $38.3M, with additional annual maintenance and operations totaling $16.1M—in addition to current ANSS expenditures for earthquake monitoring. An EEW system is complementary to, but does not replace, other strategies to mitigate earthquake losses. The system has limitations: false and missed alerts are possible, and the area very near to an earthquake epicenter may receive little or no warning. However, such an EEW system would save lives, reduce injuries and damage, and improve community resilience by reducing longer-term economic losses for both public and private entities.

The ElarmS earthquakeearlywarning (EEW) system has been successfully detecting earthquakes throughout California since 2007. ElarmS version 2.0 (E2) is one of the three algorithms contributing alerts to ShakeAlert, a public EEW system being developed by the USGS in collaboration with UC Berkeley, Caltech, University of Washington, and University of Oregon. E2 began operating in test mode in the Pacific Northwest in 2013, and since April of this year E2 has been contributing real-time alerts from Oregon and Washington to the ShakeAlert production prototype system as part of the ShakeAlert roll-out throughout the West Coast. Since it began operating west-coast-wide, E2 has correctly alerted on 5 events that matched ANSS catalog events with M≥4, missed 1 event with M≥4, and incorrectly created alerts for 5 false events with M≥4. The most recent version of the algorithm, ElarmS version 3.0 (E3), is a significant improvement over E2. It addresses some of the most problematic causes of false events for which E2 produced alerts, without impacting reliability in terms of matched and missed events. Of the 5 false events that were generated by E2 since April, 4 would have been suppressed by E3. In E3, we have added a filterbank teleseismic filter. By analyzing the amplitude of the waveform filtered in various passbands, it is possible to distinguish between local and teleseismic events. We have also added a series of checks to validate triggers and filter out spurious and S-wave triggers. Additional improvements to the waveform associator also improve detections. In this presentation, we describe the improvements and compare the performance of the current production (E2) and development (E3) versions of ElarmS over the past year. The ShakeAlert project is now working through a streamlining process to identify the best components of various algorithms and merge them. The ElarmS team is participating in this effort and we anticipate that much of E3 will continue in the

Earthquakeearlywarning (EEW) is a time-critical system and typically relies on seismic instruments in the area around the source to detect P waves (or S waves) and rapidly issue alerts. Thanks to the rapid development of real-time Global Navigation Satellite Systems (GNSS), a good number of sensors have been deployed in seismic zones, such as the western U.S. where over 600 GPS stations are collecting 1-Hz high-rate data along the Cascadia subduction zone, San Francisco Bay area, San Andreas fault, etc. GNSS sensors complement the seismic sensors by recording the static offsets while seismic data provide highly-precise higher frequency motions. An optimal combination of GNSS and accelerometer data (seismogeodesy) has advantages compared to GNSS-only or seismic-only methods and provides seismic velocity and displacement waveforms that are precise enough to detect P wave arrivals, in particular in the near source region. Robust real-time GNSS and seismogeodetic analysis is challenging because it requires a period of initialization and continuous phase ambiguity resolution. One of the limiting factors is unmodeled atmospheric effects, both of tropospheric and ionospheric origin. One mitigation approach is to introduce atmospheric corrections into precise point positioning with ambiguity resolution (PPP-AR) of clients/stations within the monitored regions. NOAA generates hourly predictions of zenith troposphere delays at an accuracy of a few centimeters, and 15-minute slant ionospheric delays of a few TECU (Total Electron Content Unit) accuracy from both geodetic and meteorological data collected at hundreds of stations across the U.S. The Scripps Orbit and Permanent Array Center (SOPAC) is experimenting with a regional ionosphere grid using a few hundred stations in southern California, and the International GNSS Service (IGS) routinely estimates a Global Ionosphere Map using over 100 GNSS stations. With these troposphere and ionosphere data as additional

To mitigate potential seismic disasters in the Yunnan region, China, building up suitable magnitude estimation scaling laws for an earthquakeearlywarning system (EEWS) is in high demand. In this paper, the records from the main and after-shocks of the Yingjiang earthquake (M W 5.9), the Ludian earthquake (M W 6.2) and the Jinggu earthquake (M W 6.1), which occurred in Yunnan in 2014, were used to develop three estimators, including the maximum of the predominant period ({{τ }{{p}}}\\max ), the characteristic period (τ c) and the log-average period (τ log), for estimating earthquake magnitude. The correlations between these three frequency-based parameters and catalog magnitudes were developed, compared and evaluated against previous studies. The amplitude and period of seismic waves might be amplified in the Ludian mountain-canyon area by multiple reflections and resonance, leading to excessive values of the calculated parameters, which are consistent with Sichuan’s scaling. As a result, τ log was best correlated with magnitude and τ c had the highest slope of regression equation, while {{τ }{{p}}}\\max performed worst with large scatter and less sensitivity for the change of magnitude. No evident saturation occurred in the case of M 6.1 and M 6.2 in this study. Even though both τ c and τ log performed similarly and can well reflect the size of the Earthquake, τ log has slightly fewer prediction errors for small scale earthquakes (M ≤ 4.5), which was also observed by previous research. Our work offers an insight into the feasibility of a EEWS in Yunnan, China, and this study shows that it is necessary to build up an appropriate scaling law suitable for the warning region.

We take into account some examples of offshore earthquakes occurred worldwide in year 2012 that were characterised by a "large" magnitude (Mw equal or larger than 7.5) but which produced no or little tsunami effects. Here, "little" is intended as "lower than expected on the basis of the parent earthquake magnitude". The examples we analyse include three earthquakes occurred along the Pacific coasts of Central America (20 March, Mw=7.8, Mexico; 5 September, Mw=7.6, Costa Rica; 7 November, Mw=7.5, Mexico), the Mw=7.6 and Mw=7.7 earthquakes occurred respectively on 31 August and 28 October offshore Philippines and offshore Alaska, and the two Indian Ocean earthquakes registered on a single day (11 April) and characterised by Mw=8.6 and Mw=8.2. For each event, we try to face the problem related to its tsunamigenic potential from two different perspectives. The first can be considered purely scientific and coincides with the question: why was the ensuing tsunami so weak? The answer can be related partly to the particular tectonic setting in the source area, partly to the particular position of the source with respect to the coastline, and finally to the focal mechanism of the earthquake and to the slip distribution on the ruptured fault. The first two pieces of information are available soon after the earthquake occurrence, while the third requires time periods in the order of tens of minutes. The second perspective is more "operational" and coincides with the tsunami earlywarning perspective, for which the question is: will the earthquake generate a significant tsunami and if so, where will it strike? The Indian Ocean events of 11 April 2012 are perfect examples of the fact that the information on the earthquake magnitude and position alone may not be sufficient to produce reliable tsunami warnings. We emphasise that it is of utmost importance that the focal mechanism determination is obtained in the future much more quickly than it is at present and that this

This paper presents firstly, the development of an integrated regional earthquakeearlywarning (EEW) system having on-line structural health monitoring (SHM) function, in Miyagi prefecture, Japan. The system makes it possible to provide more accurate, reliable and immediate earthquake information for society by combining the national (JMA/NIED) EEW system, based on advanced real-time communication technology. The author has planned to install the EEW/SHM system to the public buildings around Sendai, a million city of north-eastern Japan. The system has been so far implemented in two buildings; one is in Sendai, and the other in Oshika, a front site on the Pacific Ocean coast for the approaching Miyagi-ken Oki earthquake. The data from the front-site and the on-site are processed by the analysis system which was installed at the analysis center of Disaster Control Research Center, Tohoku University. The real-time earthquake information from JMA is also received at the analysis center. The utilization of the integrated EEW/SHM system is addressed together with future perspectives. Examples of the obtained data are also described including the amplitude depending dynamic characteristics of the building in Sendai before, during, and after the 2008/6/14 Iwate-Miyagi Nairiku Earthquake, together with the historical change of dynamic characteristics for 40 years. Secondary, this paper presents an advanced methodology based on Artificial Neural Networks (ANN) for forward forecasting of ground motion parameters, not only PGA, PGV, but also Spectral information before S-wave arrival using initial part of P-waveform at a front site. The estimated ground motion information can be used as warning alarm for earthquake damage reduction. The Fourier Amplitude Spectra (FAS) estimated before strong shaking with high accuracy can be used for advanced engineering applications, e.g. feed-forward structural control of a building of interest. The validity and applicability of the method

EWS made by NIEP is the first European system for real-time early detection and warning of the seismic waves in case of strong deep earthquakes. EWS uses the time interval (28-32 seconds) between the moment when earthquake is detected by the borehole and surface local accelerometers network installed in the epicenter area (Vrancea) and the arrival time of the seismic waves in the protected area, to deliver timely integrated information in order to enable actions to be taken before a main destructive shaking takes place. Earlywarning system is viewed as part of an real-time information system that provide rapid information, about an earthquake impeding hazard, to the public and disaster relief organizations before (earlywarning) and after a strong earthquake (shake map).This product is fitting in with other new product on way of National Institute for Earth Physics, that is, the shake map which is a representation of ground shaking produced by an event and it will be generated automatically following large Vrancea earthquakes. Bucharest City is located in the central part of the Moesian platform (age: Precambrian and Paleozoic) in the Romanian Plain, at about 140 km far from Vrancea area. Above a Cretaceous and a Miocene deposit (with the bottom at roundly 1,400 m of depth), a Pliocene shallow water deposit (~ 700m thick) was settled. The surface geology consists mainly of Quaternary alluvial deposits. Later loess covered these deposits and the two rivers crossing the city (Dambovita and Colentina) carved the present landscape. During the last century Bucharest suffered heavy damage and casualties due to 1940 (Mw = 7.7) and 1977 (Mw = 7.4) Vrancea earthquakes. For example, 32 high tall buildings collapsed and more then 1500 people died during the 1977 event. The innovation with comparable or related systems worldwide is that NIEP will use the EWS to generate a virtual shake map for Bucharest (140 km away of epicentre) immediately after the magnitude is estimated

In situ geodetic networks for observing crustal motion have proliferated over the last two decades and are now recognized as indispensable tools in geophysical research, along side more traditional seismic networks. The 2007 National Research Council’s Decadal Survey recognizes that space-borne and in situ observations, such as Interferometric Synthetic Aperture Radar (InSAR) and ground-based continuous GPS (CGPS) are complementary in forecasting, in assessing, and in mitigating natural hazards. However, the information content and timeliness of in situ geodetic observations have not been fully exploited, particularly at higher frequencies than traditional daily CGPS position time series. Nor have scientists taken full advantage of the complementary natures of geodetic and seismic data, as well as those of space-based and in situ observations. To address these deficits we are developing real-time CGPS data products for earthquakeearlywarning and for space-borne deformation measurement mission support. Our primary mission objective is in situ verification and validation for DESDynI, but our work is also applicable to other international missions (Sentinel 1a/1b, SAOCOM, ALOS 2). Our project is developing new capabilities to continuously observe and mitigate earthquake-related hazards (direct seismic damage, tsunamis, landslides, volcanoes) in near real-time with high spatial-temporal resolution, to improve the planning and accuracy of space-borne observations. We also are using GPS estimates of tropospheric zenith delay combined with water vapor data from weather models to generate tropospheric calibration maps for mitigating the largest source of error, atmospheric artifacts, in InSAR interferograms. These functions will be fully integrated into a Geophysical Resource Web Services and interactive GPS Explorer data portal environment being developed as part of an ongoing MEaSUREs project and NASA’s contribution to the EarthScope project. GPS Explorer

The key requirements for EarthquakeEarlyWarning and other Rapid Event Notification Systems are: Quick delivery of digital data from a field station to the acquisition and processing center; Data integrity for real-time earthquake notification in order to provide warning prior to significant ground shaking in the given target area. These two requirements are met in the recently developed Trimble SG160-09 SeismoGeodetic System, which integrates both GNSS and acceleration measurements using the Kalman filter algorithm to create a new high-rate (200 sps), real-time displacement with sufficient accuracy and very low latency for rapid delivery of the acquired data to a processing center. The data acquisition algorithm in the SG160-09 System provides output of both acceleration and displacement digital data with 0.2 sec delay. This is a significant reduction in the time interval required for real-time transmission compared to data delivery algorithms available in digitizers currently used in other EarthquakeEarlyWarning networks. Both acceleration and displacement data are recorded and transmitted to the processing site in a specially developed Multiplexed Recording Format (MRF) that minimizes the bandwidth required for real-time data transmission. In addition, a built in algorithm calculates the τc and Pd once the event is declared. The SG160-09 System keeps track of what data has not been acknowledged and re-transmits the data giving priority to current data. Modified REF TEK Protocol Daemon (RTPD) receives the digital data and acknowledges data received without error. It forwards this "good" data to processing clients of various real-time data processing software including Earthworm and SeisComP3. The processing clients cache packets when a data gap occurs due to a dropped packet or network outage. The cache packet time is settable, but should not exceed 0.5 sec in the EarthquakeEarlyWarning network configuration. The rapid data transmission algorithm was tested

In the earthquakeearlywarning (EEW) system, the epicenter location and magnitude of earthquakes are estimated using the amplitude growth rate of initial P-waves. It has been empirically pointed out that the growth rate becomes smaller as epicentral distance becomes far regardless of the magnitude of earthquakes. So, the epicentral distance can be estimated from the growth rate using this empirical relationship. However, the growth rates calculated from different earthquakes at the same epicentral distance mark considerably different values from each other. Sometimes the growth rates of earthquakes having the same epicentral distance vary by 104 times. Qualitatively, it has been considered that the gap in the growth rates is due to differences in the local heterogeneities that the P-waves propagate through. In this study, we demonstrate theoretically how local heterogeneities in the subsurface disturb the relationship between the growth rate and the epicentral distance. Firstly, we calculate seismic scattered waves in a heterogeneous medium. First-ordered PP, PS, SP, and SS scatterings are considered. The correlation distance of the heterogeneities and fractional fluctuation of elastic parameters control the heterogeneous conditions for the calculation. From the synthesized waves, the growth rate of the initial P-wave is obtained. As a result, we find that a parameter (in this study, correlation distance) controlling heterogeneities plays a key role in the magnitude of the fluctuation of the growth rate. Then, we calculate the regional correlation distances in Japan that can account for the fluctuation of the growth rate of real earthquakes from 1997 to 2011 observed by K-NET and KiK-net. As a result, the spatial distribution of the correlation distance shows locality. So, it is revealed that the growth rates fluctuate according to the locality. When this local fluctuation is taken into account, the accuracy of the estimation of epicentral distances from initial P

In 2002 and 2003, Seismic Warning Systems, Inc. installed first-generation QuakeGuardTM earthquakewarning devices at all eight fire stations in Vallejo, CA. These devices are designed to detect the P-wave of an earthquake and initiate predetermined protective actions if the impending shaking is estimated at approximately Modifed Mercalli Intensity V or greater. At the Vallejo fire stations the devices were set up to sound an audio alert over the public address system and to command the equipment bay doors to open. In August 2014, after more than 11 years of operating in the fire stations with no false alarms, the five units that were still in use triggered correctly on the MW 6.0 South Napa earthquake, less than 16 km away. The audio alert sounded in all five stations, providing fire fighters with 1.5 to 2.5 seconds of warning before the arrival of the S-wave, and the equipment bay doors opened in three of the stations. In one station the doors were disconnected from the QuakeGuard device, and another station lost power before the doors opened completely. These problems highlight just a small portion of the complexity associated with realizing actionable earthquakewarnings. The issues experienced in this earthquake have already been addressed in subsequent QuakeGuard product generations, with downstream connection monitoring and backup power for critical systems. The fact that the fire fighters in Vallejo were afforded even two seconds of warning at these epicentral distances results from the design of the QuakeGuard devices, which focuses on rapid false positive rejection and ground motion estimates. We discuss the performance of the ground motion estimation algorithms, with an emphasis on the accuracy and timeliness of the estimates at close epicentral distances.

The NSF-funded GAGE Facility, managed by UNAVCO, operates approximately ~1300 GNSS stations distributed across North and Central America and in the circum-Caribbean. Following community input starting in 2011 from several workshops and associated reports,UNAVCO has been exploring ways to increase the capability and utility of the geodetic resources under its management to improve our understanding in diverse areas of geophysics including properties of seismic, volcanic, magmatic and tsunami deformation sources. Networks operated by UNAVCO for the NSF have the potential to profoundly transform our ability to rapidly characterize events, provide rapid characterization and warning, as well as improve hazard mitigation and response. Specific applications currently under development include earthquakeearlywarning, tsunami earlywarning, and tropospheric modeling with university, commercial, non-profit and government partners on national and international scales. In the case of tsunami earlywarning, for example, an RT-GNSS network can provide multiple inputs in an operational system starting with rapid assessment of earthquake sources and associated deformation, which leads to the initial model of ocean forcing and tsunami generation. In addition, terrestrial GNSScan provide direct measurements of the tsunami through the associated traveling ionospheric disturbance from several 100's of km away as they approach the shoreline,which can be used to refine tsunami inundation models. Any operational system like this has multiple communities that rely on a pan-Pacific real-time open data set. Other scientific and operational applications for high-rate GPS include glacier and ice sheet motions, tropospheric modeling, and better constraints on the dynamics of space weather. Combining existing data sets and user communities, for example seismic data and tide gauge observations, with GNSS and Met data products has proven complicated because of issues related to metadata

The USGS/Caltech Southern California Seismic Network (SCSN) is a modern digital ground motion seismic network. It develops and maintains EarthquakeEarlyWarning (EEW) data collection and delivery systems in southern California as well as real-time EEW algorithms. Recently, Behr et al., SRL, 2016 analyzed data from several regional seismic networks deployed around the globe. They showed that the SCSN was the network with the smallest data communication delays or latency. Since then, we have reduced further the telemetry delays for many of the 330 current sites. The latency has been reduced on average from 2-6 sec to 0.4 seconds by tuning the datalogger parameters and/or deploying software upgrades. Recognizing the latency data as one of the crucial parameters in EEW, we have started archiving the per-packet latencies in mseed format for all the participating sites in a similar way it is traditionally done for the seismic waveform data. The archived latency values enable us to understand and document long-term changes in performance of the telemetry links. We can also retroactively investigate how latent the waveform data were during a specific event or during a specific time period. In addition the near-real time latency values are useful for monitoring and displaying the real-time station latency, in particular to compare different telemetry technologies. A future step to reduce the latency is to deploy the algorithms on the dataloggers at the seismic stations and transmit either the final solutions or intermediate parameters to a central processing center. To implement this approach, we are developing a stand-alone version of the OnSite algorithm to run on the dataloggers in the field. This will increase the resiliency of the SCSN to potential telemetry restrictions in the immediate aftermath of a large earthquake, either by allowing local alarming by the single station, or permitting transmission of lightweight parametric information rather than continuous

Severe maternal morbidity and mortality are often preventable and obstetric earlywarning systems that alert care providers of potential impending critical illness may improve maternal safety. While literature on outcomes and test characteristics of maternal earlywarning systems is evolving, there is limited guidance on implementation. Given current interest in earlywarning systems and their potential role in care, the 2017 Society for Maternal-Fetal Medicine (SMFM) Annual Meeting dedicated a session to exploring earlywarning implementation across a wide range of hospital settings. This manuscript reports on key points from this session. While implementation experiences varied based on factors specific to individual sites, common themes relevant to all hospitals presenting were identified. Successful implementation of earlywarnings systems requires administrative and leadership support, dedication of resources, improved coordination between nurses, providers, and ancillary staff, optimization of information technology, effective education, evaluation of and change in hospital culture and practices, and support in provider decision-making. Evolving data on outcomes on earlywarning systems suggest that maternal risk may be reduced. To effectively reduce maternal, risk earlywarning systems that capture deterioration from a broad range of conditions may be required in addition to bundles tailored to specific conditions such as hemorrhage, thromboembolism, and hypertension.

We have implemented an on-site earlywarning algorithm using the infrastructure of the Caltech/USGS Southern California Seismic Network (SCSN). We are evaluating the real-time performance of the software system and the algorithm for rapid assessment of earthquakes. In addition, we are interested in understanding what parts of the SCSN need to be improved to make earlywarning practical. Our EEW processing system is composed of many independent programs that process waveforms in real-time. The codes were generated by using a software framework. The Pd (maximum displacement amplitude of P wave during the first 3sec) and Tau-c (a period parameter during the first 3 sec) values determined during the EEW processing are being forwarded to the California Integrated Seismic Network (CISN) web page for independent evaluation of the results. The on-site algorithm measures the amplitude of the P-wave (Pd) and the frequency content of the P-wave during the first three seconds (Tau-c). The Pd and the Tau-c values make it possible to discriminate between a variety of events such as large distant events, nearby small events, and potentially damaging nearby events. The Pd can be used to infer the expected maximum ground shaking. The method relies on data from a single station although it will become more reliable if readings from several stations are associated. To eliminate false triggers from stations with high background noise level, we have created per station Pd threshold configuration for the Pd/Tau-c algorithm. To determine appropriate values for the Pd threshold we calculate Pd thresholds for stations based on the information from the EEW logs. We have operated our EEW test system for about a year and recorded numerous earthquakes in the magnitude range from M3 to M5. Two recent examples are a M4.5 earthquake near Chatsworth and a M4.7 earthquake near Elsinore. In both cases, the Pd and Tau-c parameters were determined successfully within 10 to 20 sec of the arrival of the

The Virtual Seismologist (VS) earthquakeearlywarning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system being tested in real-time in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network (SCSN) since July 2008, and at the Northern California Seismic Network (NCSN) since February 2009. With the aim of improving the convergence of real-time VS magnitude estimates to network magnitudes, we evaluate various empirical and Vs30-based approaches to accounting for site amplification. Empirical station corrections for SCSN stations are derived from M>3.0 events from 2005 through 2009. We evaluate the performance of the various approaches using an independent 2010 dataset. In addition, we analyze real-time VS performance from 2008 to the present to quantify the time and spatial dependence of VS uncertainty estimates. We also summarize real-time VS performance for significant 2011 events in California. Improved magnitude and uncertainty estimates potentially increase the utility of EEW information for end-users, particularly those intending to automate damage-mitigating actions based on real-time information.

This study analyzes the response of the Global Disasters Alerts and Coordination System (GDACS) in relation to a case study: the Kepulaunan Mentawai earthquake and related tsunami, which occurred on 25 October 2010. The GDACS, developed by the European Commission Joint Research Center, combines existing web-based disaster information management systems with the aim to alert the international community in case of major disasters. The tsunami simulation system is an integral part of the GDACS. In more detail, the study aims to assess the tsunami hazard on the Mentawai and Sumatra coasts: the tsunami heights and arrival times have been estimated employing three propagation models based on the long wave theory. The analysis was performed in three stages: (1) pre-calculated simulations by using the tsunami scenario database for that region, used by the GDACS system to estimate the alert level; (2) near-real-time simulated tsunami forecasts, automatically performed by the GDACS system whenever a new earthquake is detected by the seismological data providers; and (3) post-event tsunami calculations using GCMT (Global Centroid Moment Tensor) fault mechanism solutions proposed by US Geological Survey (USGS) for this event. The GDACS system estimates the alert level based on the first type of calculations and on that basis sends alert messages to its users; the second type of calculations is available within 30-40 min after the notification of the event but does not change the estimated alert level. The third type of calculations is performed to improve the initial estimations and to have a better understanding of the extent of the possible damage. The automatic alert level for the earthquake was given between Green and Orange Alert, which, in the logic of GDACS, means no need or moderate need of international humanitarian assistance; however, the earthquake generated 3 to 9 m tsunami run-up along southwestern coasts of the Pagai Islands where 431 people died. The post

USING AN EARLYwarning score (EWS) system should improve the detection of acutely deteriorating patients. Under such a system, a score is allocated to each of six physiological measurements including respiratory rate and oxygen saturations, which are aggregated to produce an overall score. An aggregated score of seven or higher prompts nursing staff to refer a patient for emergency assessment.

The earthquakeearlywarning (EEW) systems in California and elsewhere can greatly benefit from algorithms that generate estimates of finite-fault parameters. These estimates could significantly improve real-time shaking calculations and yield important information for immediate disaster response. Minson et al. (2015) determined that combining FinDer's seismic-based algorithm (Böse et al., 2012) with BEFORES' geodetic-based algorithm (Minson et al., 2014) yields a more robust and informative joint solution than using either algorithm alone. FinDer examines the distribution of peak ground accelerations from seismic stations and determines the best finite-fault extent and strike from template matching. BEFORES employs a Bayesian framework to search for the best slip inversion over all possible fault geometries in terms of strike and dip. Using FinDer and BEFORES together generates estimates of finite-fault extent, strike, dip, preferred slip, and magnitude. To yield the quickest, most flexible, and open-source version of the joint algorithm, we translated BEFORES and FinDer from Matlab into C++. We are now developing a C++ Application Protocol Interface for these two algorithms to be connected to the seismic and geodetic data flowing from the EEW system. The interface that is being developed will also enable communication between the two algorithms to generate the joint solution of finite-fault parameters. Once this interface is developed and implemented, the next step will be to run test seismic and geodetic data through the system via the Earthworm module, Tank Player. This will allow us to examine algorithm performance on simulated data and past real events.

High-rate GPS can play an important role in earthquakeearlywarning (EEW) systems for large (>M6) events by providing permanent displacements immediately as they are achieved, to be used in source inversions that can be repeatedly updated as more information becomes available. This is most valuable to implement at a site very near the potential source rupture, where broadband seismometers are likely to clip, and accelerometer data cannot be objectively integrated to produce reliable displacements in real time. At present, more than 525 real-time GPS stations have been established in western North America, which are being integrated into EEW systems. Our analysis technique relies on a tightly-coupled combination of GPS and accelerometer data, an extension of precise point positioning with ambiguity resolution (PPP-AR). We operate a PPP service based on North American stations available through the IGS and UNAVCO/PBO. The service provides real-time satellite clock and fractional-cycle bias products that allow us to position individual client stations in the zone of deformation. The service reference stations are chosen to be further than 200 km from the primary zones of tectonic deformation in the western U.S. to avoid contamination of the satellite products during a large seismic event. At client stations, accelerometer data are applied as tight constraints on the positions between epochs in PPP-AR, which improves cycle-slip repair and rapid ambiguity resolution after GPS outages. Furthermore, we estimate site displacements, seismic velocities, and coseismic ground tilts to facilitate the analysis of ground motion characteristics and the inversion for source mechanisms. The seismogeodetic displacement and velocity waveforms preserves the detection of P wave arrivals, and provides P-wave arrival displacement that is key new information for EEW. Our innovative solution method for coseismic tilts mitigates an error source that has continually plagued strong motion

Tsunamis are most destructive at near to regional distances, arriving within 20-30 min after a causative earthquake; effective earlywarning at these distances requires notification within 15 min or less. The size and impact of a tsunami also depend on sea floor displacement, which is related to the length, L, width, W, mean slip, D, and depth, z, of the earthquake rupture. Currently, the primary seismic discriminant for tsunami potential is the centroid-moment tensor magnitude, M {w/CMT}, representing the product LWD and estimated via an indirect inversion procedure. However, the obtained M {w/CMT} and the implied LWD value vary with rupture depth, earth model, and other factors, and are only available 20-30 min or more after an earthquake. The use of more direct discriminants for tsunami potential could avoid these problems and aid in effective earlywarning, especially for near to regional distances. Previously, we presented a direct procedure for rapid assessment of earthquake tsunami potential using two, simple measurements on P-wave seismograms—the predominant period on velocity records, T d , and the likelihood, T {50/Ex}, that the high-frequency, apparent rupture-duration, T 0, exceeds 50-55 s. We have shown that T d and T 0 are related to the critical rupture parameters L, W, D, and z, and that either of the period-duration products T d T 0 or T d T {50/Ex} gives more information on tsunami impact and size than M {w/CMT}, M wp, and other currently used discriminants. These results imply that tsunami potential is not directly related to the product LWD from the "seismic" faulting model, as is assumed with the use of the M {w/CMT} discriminant. Instead, information on rupture length, L, and depth, z, as provided by T d T 0 or T d T {50/Ex}, can constrain well the tsunami potential of an earthquake. We introduce here special treatment of the signal around the S arrival at close stations, a modified, real-time, M wpd(RT) magnitude, and other procedures to

Scientific GNSS networks are moving towards a model of real-time data acquisition, epoch-by-epoch storage integrity, and on-board real-time position and displacement calculations. This new paradigm allows the integration of real-time, high-rate GNSS displacement information with acceleration and velocity data to create very high-rate displacement records. The mating of these two instruments allows the creation of a new, very high-rate (200 sps) displacement observable that has the full-scale displacement characteristics of GNSS and high-precision dynamic motions of seismic technologies. It is envisioned that these new observables can be used for earthquakeearlywarning studies, volcano monitoring, and critical infrastructure monitoring applications. Our presentation will focus on the characteristics of GNSS, seismic, and strong motion sensors in high dynamic environments, including historic earthquakes in Southern California and the Pacific Rim, replicated on a shake table, over a range of displacements and frequencies. We will explore the optimum integration of these sensors from a filtering perspective including simple harmonic impulses over varying frequencies and amplitudes and under the dynamic conditions of various earthquake scenarios. In addition we will discuss implementation of a Rapid Seismic Event Notification System that provides quick delivery of digital data from seismic stations to the acquisition and processing center and a full data integrity model for real-time earthquake notification that provides warning prior to significant ground shaking.

In the frame of a collaboration between the European Commission Joint Research Centre and the Institute of Meteorology in Portugal, a complete analytical tool to support EarlyWarning Systems is being developed. The tool will be part of the Portuguese National EarlyWarning System and will be used also in the frame of the UNESCO North Atlantic Section of the Tsunami EarlyWarning System. The system called Tsunami Analysis Tool (TAT) includes a worldwide scenario database that has been pre-calculated using the SWAN-JRC code (Annunziato, 2007). This code uses a simplified fault generation mechanism and the hydraulic model is based on the SWAN code (Mader, 1988). In addition to the pre-defined scenario, a system of computers is always ready to start a new calculation whenever a new earthquake is detected by the seismic networks (such as USGS or EMSC) and is judged capable to generate a Tsunami. The calculation is performed using minimal parameters (epicentre and the magnitude of the earthquake): the programme calculates the rupture length and rupture width by using empirical relationship proposed by Ward (2002). The database calculations, as well the newly generated calculations with the current conditions are therefore available to TAT where the real online analysis is performed. The system allows to analyze also sea level measurements available worldwide in order to compare them and decide if a tsunami is really occurring or not. Although TAT, connected with the scenario database and the online calculation system, is at the moment the only software that can support the tsunami analysis on a global scale, we are convinced that the fault generation mechanism is too simplified to give a correct tsunami prediction. Furthermore short tsunami arrival times especially require a possible earthquake source parameters data on tectonic features of the faults like strike, dip, rake and slip in order to minimize real time uncertainty of rupture parameters. Indeed the earthquake

The past decade has witnessed a terrible loss of life and economic disruption caused by large earthquakes and resultant tsunamis impacting coastal communities and infrastructure across the Indo-Pacific region. NASA has funded the early development of a prototype real-time Global Navigation Satellite System (RT-GNSS) based rapid earthquake and tsunami earlywarning (GNSS-TEW) system that may be used to enhance seismic tsunami earlywarning systems for large earthquakes. This prototype GNSS-TEW system geodetically estimates fault parameters (earthquake magnitude, location, strike, dip, and slip magnitude/direction on a gridded fault plane both along strike and at depth) and tsunami source parameters (seafloor displacement, tsunami energy scale, and 3D tsunami initials) within minutes after the mainshock based on dynamic numerical inversions/regressions of the real-time measured displacements within a spatially distributed real-time GNSS network(s) spanning the epicentral region. It is also possible to measure fluctuations in the ionosphere's total electron content (TEC) in the RT-GNSS data caused by the pressure wave from the tsunami. This TEC approach can detect if a tsunami has been triggered by an earthquake, track its waves as they propagate through the oceanic basins, and provide upwards of 45 minutes earlywarning. These combined real-time geodetic approaches will very quickly address a number of important questions in the immediate minutes following a major earthquake: How big was the earthquake and what are its fault parameters? Could the earthquake have produced a tsunami and was a tsunami generated?

Tsunami wave predictions of the current tsunami warning systems rely on accurate earthquake source inversions of wave height data. They are of limited effectiveness for the near-field areas since the tsunami waves arrive before data are collected. Recent seismic and tsunami disasters have revealed the need for earlywarning to protect near-source coastal populations. In this work we developed the basis for a tsunami warning system based on rapid earthquake source characterisation through regional seismic array back-projections. We explored rapid earthquake source imaging using onshore dense seismic arrays located at regional distances on the order of 1000 km, which provides faster source images than conventional teleseismic back-projections. We implement this method in a simulated real-time environment, and analysed the 2011 Tohoku earthquake rupture with two clusters of Hi-net stations in Kyushu and Northern Hokkaido, and the 2014 Iquique event with the Earthscope USArray Transportable Array. The results yield reasonable estimates of rupture area, which is approximated by an ellipse and leads to the construction of simple slip models based on empirical scaling of the rupture area, seismic moment and average slip. The slip model is then used as the input of the tsunami simulation package COMCOT to predict the tsunami waves. In the example of the Tohoku event, the earthquake source model can be acquired within 6 minutes from the start of rupture and the simulation of tsunami waves takes less than 2 min, which could facilitate a timely tsunami warning. The predicted arrival time and wave amplitude reasonably fit observations. Based on this method, we propose to develop an automatic warning mechanism that provides rapid near-field warning for areas of high tsunami risk. The initial focus will be Japan, Pacific Northwest and Alaska, where dense seismic networks with the capability of real-time data telemetry and open data accessibility, such as the Japanese HiNet (>800

which affect taller , multi-story buildings. Ground motion that affects shorter buildings of a few stories, called short-period seismic waves, is...places in a single fault, or jump between connected faults. Earthquakes that occur along the Sierra Madre fault in southern California, for example

Several aerospace companies are designing quiet supersonic business jets for service over the United States. These aircraft have the potential to increase the occurrence of mild sonic booms across the country. This leads to interest among earthquakewarning (EQW) developers and the general seismological community in characterizing the effect of sonic booms on seismic sensors in the field, their potential impact on EQW systems, and means of discriminating their signatures from those of earthquakes. The SonicBREWS project (Sonic Boom Resistant EarthquakeWarning Systems) is a collaborative effort between Seismic Warning Systems, Inc. (SWS) and NASA Dryden Flight Research Center. This project aims to evaluate the effects of sonic booms on EQW sensors. The study consists of exposing high-sample-rate (1000 sps) triaxial accelerometers to sonic booms with overpressures ranging from 10 to 600 Pa in the free field and the built environment. The accelerometers record the coupling of the sonic boom to the ground and surrounding structures, while microphones record the acoustic wave above ground near the sensor. Sonic booms are broadband signals with more high-frequency content than earthquakes. Even a 1000 sps accelerometer will produce a significantly aliased record. Thus the observed peak ground velocity is strongly dependent on the sampling rate, and increases as the sampling rate is reduced. At 1000 sps we observe ground velocities that exceed those of P-waves from ML 3 earthquakes at local distances, suggesting that sonic booms are not negligible for EQW applications. We present the results of several experiments conducted under SonicBREWS showing the effects of typical-case low amplitude sonic booms and worst-case high amplitude booms. We show the effects of various sensor placements and sensor array geometries. Finally, we suggest possible avenues for discriminating sonic booms from earthquakes for the purposes of EQW.

This articles summarizes the National Planning Association's (NPA) experience in its initial efforts to develop an earlywarning system to anticipate job openings generated in local communities by large Federal procurement contracts. (WL)

The societal impact of geological hazards is enormous. Every year volcanoes, earthquakes, landslides and subsidence claim thousands of lives, injure many thousands more, devastate peoples' homes and destroy their livelihoods. The costs of damaged infrastructure are taken higher still by insurance premiums and run into the billions in any currency. This affects rich and poor alike, but with a disproportionate impact on the developing world. As the human population increases and more people live in hazardous areas, this impact grows unsustainably. It must be reduced and that requires increased understanding of the geohazards, improved preparedness for disasters and better ways to manage them when they occur. The inter-related disasters that comprise geohazards are all driven directly by geological processes and share ground deformation as a common thread. This means that they can be addressed using similar technology and understood using related scientific modelling processes. Geohazards are a complex phenomenon and no one method can provide all the necessary information and understanding. It is essential that Earth Observation data are integrated with airborne data, in-situ observations and associated historical data archives, and then analysed using GIS and other modelling tools if these hazards are to be understood and managed. Geohazards occur in one form or another in every country. They do not respect national boundaries and have the potential to cause changes in the atmosphere that will be truly global in effect, requiring a global observing infrastructure to monitor them. The current situation in space research of earlywarning of geohazards indicates a few phenomena, related with geohazard processes: Earth's deformation, surface temperature, gas and aerosol emission, electromagnetic disturbances in ionosphere. Both horizontal and vertical deformations scaled about tens centimetres and meters measured after the shock. Such deformations are recorded by In

We present a method for estimating the source duration of the fault rupture, based on the high-frequency envelop of teleseismic P-Waves, inspired from the original work of (Ni et al., 2005). The main interest of the knowledge of this seismic parameter is to detect abnormal low velocity ruptures that are the characteristic of the so called 'tsunami-earthquake' (Kanamori, 1972). The validation of the results of source duration estimated by this method are compared with two other independent methods : the estimated duration obtained by the Wphase inversion (Kanamori and Rivera, 2008, Duputel et al., 2012) and the duration calculated by the SCARDEC process that determines the source time function (M. Vallée et al., 2011). The estimated source duration is also confronted to the slowness discriminant defined by Newman and Okal, 1998), that is calculated routinely for all earthquakes detected by our tsunami warning process (named PDFM2, Preliminary Determination of Focal Mechanism, (Clément and Reymond, 2014)). Concerning the point of view of operational tsunami warning, the numerical simulations of tsunami are deeply dependent on the source estimation: better is the source estimation, better will be the tsunami forecast. The source duration is not directly injected in the numerical simulations of tsunami, because the cinematic of the source is presently totally ignored (Jamelot and Reymond, 2015). But in the case of a tsunami-earthquake that occurs in the shallower part of the subduction zone, we have to consider a source in a medium of low rigidity modulus; consequently, for a given seismic moment, the source dimensions will be decreased while the slip distribution increased, like a 'compact' source (Okal, Hébert, 2007). Inversely, a rapid 'snappy' earthquake that has a poor tsunami excitation power, will be characterized by higher rigidity modulus, and will produce weaker displacement and lesser source dimensions than 'normal' earthquake. References: CLément, J

Through independent efforts, physics-based simulations of earthquakes, tsunamis, and atmospheric signatures of these phenomenon have been developed. With the goal of producing tsunami forecasts and earlywarning tools for at-risk regions, we join these three spheres to create a simulation pipeline. The Virtual Quake simulator can produce thousands of years of synthetic seismicity on large, complex fault geometries, as well as the expected surface displacement in tsunamigenic regions. These displacements are used as initial conditions for tsunami simulators, such as Tsunami Squares, to produce catalogs of potential tsunami scenarios with probabilities. Finally, these tsunami scenarios can act as input for simulations of associated ionospheric total electron content, signals which can be detected by GNSS satellites for purposes of earlywarning in the event of a real tsunami. We present the most recent developments in this project.

EarlyWarning Inc. of Troy, New York, licensed powerful biosensor technology from Ames Research Center. Incorporating carbon nanotubes tipped with single strands of nucleic acid from waterborne pathogens, the sensor can detect even minute amounts of targeted, disease causing bacteria, viruses, and parasites. EarlyWarning features the NASA biosensor in its water analyzer, which can provide advance alert of potential biological hazards in water used for agriculture, food and beverages, showers, and at beaches and lakes -- within hours instead of the days required by conventional laboratory methods.

Robert Kovach's second book looks at the interplay of earthquake and volcanic events, archeology, and history in the Americas. Throughout history, major earthquakes have caused the deaths of millions of people and have damaged countless cities. Earthquakes undoubtedly damaged prehistoric cities in the Americas, and evidence of these events could be preserved in archeological records. Kovach asks, Did indigenous native cultures-Indians of the Pacific Northwest, Aztecs, Mayas, and Incas-document their natural history? Some events have been explicitly documented, for example, in Mayan codices, but many may have been recorded as myth and legend. Kovach's discussions of how early cultures dealt with fearful events such as earthquakes and volcanic eruptions are colorful, informative, and entertaining, and include, for example, a depiction of how the Maya would talk to maize plants in their fields during earthquakes to reassure them.

SAFER and EDIM working groups, the Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany, and Section 2.1 Earthquake Risk and EarlyWarning, GFZ German Research Centre for Geosciences, Germany Contact: Frank Kühnlenz, kuehnlenz@informatik.hu-berlin.de The Self-Organising Seismic EarlyWarning Information Network (SOSEWIN) represents a new approach for EarthquakeEarlyWarning Systems (EEWS), consisting in taking advantage of novel wireless communications technologies without the need of a planned, centralised infrastructure. It also sets out to overcome problems of insufficient node density, which typically affects present existing earlywarning systems, by having the SOSEWIN seismological sensing units being comprised of low-cost components (generally bought "off-the-shelf"), with each unit initially costing 100's of Euros, in contrast to 1,000's to 10,000's for standard seismological stations. The reduced sensitivity of the new sensing units arising from the use of lower-cost components will be compensated by the network's density, which in the future is expected to number 100's to 1000's over areas served currently by the order of 10's of standard stations. The robustness, independence of infrastructure, spontaneous extensibility due to a self-healing/self-organizing character in the case of removing/failing or adding sensors makes SOSEWIN potentially useful for various use cases, e.g. monitoring of building structures or seismic microzonation. Nevertheless its main purpose is the earthquakeearlywarning, for which reason the ground motion is continuously monitored by conventional accelerometers (3-component). It uses SEEDLink to store and provide access to the sensor data. SOSEWIN considers also the needs of earthquake task forces, which want to set-up a temporary seismic network rapidly and with light-weighted stations to record after-shocks. The wireless and self-organising character of this sensor network should be of great value

Following the tsunami disaster in 2004, the General Secretary of the United Nations (UN) Kofi Annan called for a global earlywarning system for all hazards and for all communities. He also requested the ISDR (International Strategy fort Disaster Reduction) and its UN partners to conduct a global survey of capacities, gaps and opportunities in relation to earlywarning systems. The produced report, "Global survey of EarlyWarning Systems", concluded that there are many gaps and shortcomings and that much progress has been made on earlywarning systems and great capabilities are available around the world. However, it may be argued that an earlywarning system (EWS) may not be enough to prevent fatalities due to a natural hazard; i.e., it should be seen as part of a ‘wider' or total system. Furthermore, an EWS may work very well when assessed individually but it is not clear whether it will contribute to accomplish the purpose of the ‘total disaster management system'; i.e., to prevent fatalities. For instance, a regional EWS may only work if it is well co-ordinated with the local warning and emergency response systems that ensure that the warning is received, communicated and acted upon by the potentially affected communities. It may be argued that without these local measures being in place, a regional EWS will have little impact in saving lives. Researchers argued that unless people are warned in remote areas, the technology is useless; for instance McGuire [5] argues that: "I have no doubt that the technical element of the warning system will work very well,"…"But there has to be an effective and efficient communications cascade from the warning centre to the fisherman on the beach and his family and the bar owners." Similarly, McFadden [6] states that: "There's no point in spending all the money on a fancy monitoring and a fancy analysis system unless we can make sure the infrastructure for the broadcast system is there,"… "That's going to require a lot

Earthquake represents a major natural disaster for Romanian territory. The main goal following the occurrence of a strong earthquake is to minimize the total number of fatalities. A rapid earlywarning system (REWS) was developed in Romania in order to provide 25-35 seconds warning time to Bucharest facilities for the earthquakes with M>5.0. The system consists of four components: a network of strong motion sensors installed in the epicentral area, a redundant communication network, an automatic analyzing system located in the Romanian Data Centre and an alert distribution system. The detection algorithm is based on the magnitude computation using strong motion data and rapid evaluation and scaling relation between the maximum P-wave acceleration measured in the epicentral area and the higher ground motion amplitude recorded in Bucharest. In order to reduce the damages caused by earthquakes, the exploitation of the up to date technology is very important. The information is the key point in the disaster management, and the internet is one of the most used instrument, implying also low costs. The Rapid EarlyWarning System was expanded to cover all countries affected by major earthquakes originating in the Vrancea seismic area and reduce their impact on existing installations of national interest in neighbouring Romania and elsewhere. REWS provides an efficient instrument for prevention and reaction based on the integrated system for seismic detection in South-Eastern Europe. REWS has been operational since 2013 and sends alert the authorities, hazardous facilities in Romania and Bulgaria (NPP, emergency response agencies etc.) and to public via twitter and some smartphone applications developed in the house. Also, NIEP is part of the UNESCO initiative case on developing a platform on earthquakeearlywarning systems (IP-MEP) that aims to promote and strengthen the development of earthquakeearlywarning systems in earthquake-prone regions of the world by sharing

TrigDB is initially developed for the discrimination of teleseismic-origin false alarm in the case with unreasonably associated triggers producing mis-located epicenters. We have applied TrigDB to the current EEWS(EarthquakeEarlyWarning System) from 2014. During the early stage of testing EEWS from 2011, we adapted ElarmS from US Berkeley BSL to Korean seismic network and applied more than 5 years. We found out that the real-time testing results of EEWS in Korea showed that all events inside of seismic network with bigger than magnitude 3.0 were well detected. However, two events located at sea area gave false location results with magnitude over 4.0 due to the long period and relatively high amplitude signals related to the teleseismic waves or regional deep sources. These teleseismic-relevant false events were caused by logical co-relation during association procedure and the corresponding geometric distribution of associated stations is crescent-shaped. Seismic stations are not deployed uniformly, so the expected bias ratio varies with evaluated epicentral location. This ratio is calculated in advance and stored into database, called as TrigDB, for the discrimination of teleseismic-origin false alarm. We upgraded this method, so called `TrigDB back filling', updating location with supplementary association of stations comparing triggered times between sandwiched stations which was not associated previously based on predefined criteria such as travel-time. And we have tested a module to reject outlier trigger times by setting a criteria comparing statistical values(Sigma) to the triggered times. The criteria of cutting off the outlier is slightly slow to work until the number of stations more than 8, however, the result of location is very much improved.

The Self-Organising Seismic EarlyWarning Information Network (SOSEWIN) represents a new approach for EarthquakeEarlyWarning Systems (EEWS), consisting in taking advantage of novel wireless communications technologies without the need of a planned, centralised infrastructure. It also sets out to overcome problems of insufficient node density, which typically affects present existing earlywarning systems, by having the SOSEWIN seismological sensing units being comprised of low-cost components (generally bought "off-the-shelf"), with each unit initially costing 100's of Euros, in contrast to 1,000's to 10,000's for standard seismological stations. The reduced sensitivity of the new sensing units arising from the use of lower-cost components will be compensated by the network's density, which in the future is expected to number 100's to 1000's over areas served currently by the order of 10's of standard stations. The robustness, independence of infrastructure, spontaneous extensibility due to a self-healing/self-organizing character in the case of removing/failing or adding sensors makes SOSEWIN potentially useful for various use cases, e.g. monitoring of building structures or seismic microzonation. Nevertheless its main purpose is the earthquakeearlywarning, for which reason the ground motion is continuously monitored by conventional accelerometers (3-component) and processed within a station. Based on this, the network itself decides whether an event is detected through cooperating stations. SEEDLink is used to store and provide access to the sensor data. Experiences and selected experiment results with the SOSEWIN-prototype installation in the Ataköy district of Istanbul (Turkey) are presented. SOSEWIN considers also the needs of earthquake task forces, which want to set-up a temporary seismic network rapidly and with light-weighted stations to record after-shocks. The wireless and self-organising character of this sensor network is of great value to do this

An earthquake happens when two blocks of the earth suddenly slip past one another. Earthquakes strike suddenly, violently, and without warning at any time of the day or night. If an earthquake occurs in a populated area, it may cause ...

As nurses, we represent the backbone of the health care system. It is essential that we have a core understanding of infectious disease emergencies and begin to use the strengths that characterize nursing. These strengths include the ability to evaluate situations and use evidence on which to base our actions. Early identification of an infectious disease emergency is one example of using nursing skills to strengthen emergency preparedness. During an infectious disease emergency, nurses certainly will bear the burden of patient management. Because of this, the need for infectious disease emergency preparedness has become a national priority and a moral imperative for all nurses. One topic necessary for ED and OH nurses' preparedness has been discussed in this article, but nurses must take the initiative to learn more about disaster preparedness and incorporate these skills into everyday practice.

Scientific organizations like the United States Geological Survey (USGS) release information to support effective responses during an earthquake crisis. Information is delivered to the White House, the National Command Center, the Departments of Defense, Homeland Security (including FEMA), Transportation, Energy, and Interior. Other crucial stakeholders include state officials and decision makers, emergency responders, numerous public and private infrastructure management centers (e.g., highways, railroads and pipelines), the media, and the public. To meet the diverse information requirements of these users, rapid earthquake notifications have been developed to be delivered by e-mail and text message, as well as a suite of earthquake information resources such as ShakeMaps, Did You Feel It?, PAGER impact estimates, and data are delivered via the web. The ShakeAlert earthquakeearlywarning system being developed for the U.S. West Coast will identify and characterize an earthquake a few seconds after it begins, estimate the likely intensity of ground shaking, and deliver brief but critically important warnings to people and infrastructure in harm's way. Currently the USGS is also developing a capability to deliver Operational Earthquake Forecasts (OEF). These provide estimates of potential seismic behavior after large earthquakes and during evolving aftershock sequences. Similar work is underway in New Zealand, Japan, and Italy. In the development of OEF forecasts, social science research conducted during these sequences indicates that aftershock forecasts are valued for a variety of reasons, from informing critical response and recovery decisions to psychologically preparing for more earthquakes. New tools will allow users to customize map-based, spatiotemporal forecasts to their specific needs. Hazard curves and other advanced information will also be available. For such authoritative information to be understood and used during the pressures of an earthquake

On Boxing Day 2004, a severe tsunami was generated by a strong earthquake in Northern Sumatra causing a large number of casualties. At this time, neither an offshore buoy network was in place to measure tsunami waves, nor a system to disseminate tsunami warnings to local governmental entities. Since then, buoys have been developed by Indonesia and Germany, complemented by NOAA's Deep-ocean Assessment and Reporting of Tsunamis (DART) buoys, and have been moored offshore Sumatra and Java. The suite of sensors for offshore tsunami detection in Indonesia has been advanced by adding GPS technology for water level measurements. The usage of GPS buoys in tsunami warning systems is a relatively new approach. The concept of the German Indonesian Tsunami EarlyWarning System (GITEWS) (Rudloff et al., 2009) combines GPS technology and ocean bottom pressure (OBP) measurements. Especially for near-field installations where the seismic noise may deteriorate the OBP data, GPS-derived sea level heights provide additional information. The GPS buoy technology is precise enough to detect medium to large tsunamis of amplitudes larger than 10 cm. The analysis presented here suggests that for about 68% of the time, tsunamis larger than 5 cm may be detectable.

Thanks to its global coverage, its peacetime capabilities and its availability, ballistic missiles EarlyWarning (EW) space systems are identified as a key node of a global missile defence system. Since the Gulf war in 1991, several feasibility studies of such an EarlyWarning system have been conducted in France. The main conclusions are first that the most appropriate concept is to use infra-red (IR) sensors on geo- stationary orbit satellites and second that the required satellite performances are achievable and accessible to European industries, even if technological developments are necessary. Besides that, it was recommended to prepare the development of the EW operational system, by demonstrating its achievable performances on the basis of collected background images and available target IR signatures. This is the objective of the "EW optical space demonstrator", also named SPIRALE (this a French acronym which stands for "Preparatory IR Program for EW"). A contract has been awarded early 2004, by DGA/SPOTI (French Armament Procurement Agency), to EADS Astrium France, with a significant participation of Alcatel Space, to perform this demonstration.

The management of natural crises is an important application field of the technology developed in the project Collaborative, Complex, and Critical Decision-Support in Evolving Crises (TRIDEC), co-funded by the European Commission in its Seventh Framework Programme. TRIDEC is based on the development of the German Indonesian Tsunami EarlyWarning System (GITEWS) and the Distant EarlyWarning System (DEWS) providing a service platform for both sensor integration and warning dissemination. In TRIDEC new developments in Information and Communication Technology (ICT) are used to extend the existing platform realising a component-based technology framework for building distributed tsunami warning systems for deployment, e.g. in the North-eastern Atlantic, the Mediterranean and Connected Seas (NEAM) region. The TRIDEC system will be implemented in three phases, each with a demonstrator. Successively, the demonstrators are addressing challenges, such as the design and implementation of a robust and scalable service infrastructure supporting the integration and utilisation of existing resources with accelerated generation of large volumes of data. These include sensor systems, geo-information repositories, simulation tools and data fusion tools. In addition to conventional sensors also unconventional sensors and sensor networks play an important role in TRIDEC. The system version presented is based on service-oriented architecture (SOA) concepts and on relevant standards of the Open Geospatial Consortium (OGC), the World Wide Web Consortium (W3C) and the Organization for the Advancement of Structured Information Standards (OASIS). In this way the system continuously gathers, processes and displays events and data coming from open sensor platforms to enable operators to quickly decide whether an earlywarning is necessary and to send personalized warning messages to the authorities and the population at large through a wide range of communication channels. The system

Disaster-resilient communities are communities capable of anticipating and minimizing destructive forces through adaptation. Disaster is an event very close to the people of Indonesia, especially in the small tourism town of Pangadaran located at West Java, Indonesia. On July 17, 2006, the town was hit by a Mw 7.8 earthquake and tsunami that effected over 300 km of the coastline, where the community suffered losses in which more than 600 people were killed, with run up heights exceeding 20 m. The devastation of the tsunami have made the community more alert and together with the local government and other stakeholder develop an EarlyWarning System for Tsunami. The study is intended to discover issues on tsunami EarlyWarning System (EWS), disaster risk reduction measures taken and community participation. The research method used is descriptive and explanatory research. The study describe the Tsunami EWS and community based Disaster Risk Reduction in Pangandaran, the implementation of Tsunami alert/EWS in disaster preparedness and observation of community participation in EWS. Data were gathered by secondary data collection, also primary data through interviews, focus group discussions and field observations. Research resulted in a description of EWS implementation, community participation and recommendation to reduce disaster risk in Pangandaran.

Earlywarning system (EWS) is the most effective method in saving lives and reducing property damages resulted from the catastrophic landslides if properly implemented in populated areas of landslide-prone nations. For predicting the occurrence of landslides, it requires examination of empirical relationship between rainfall characteristics and past landslide occurrence. In developed countries like Japan and the US, precipitation is monitored by rain radars and ground-based rain gauge matrix. However, in developing regions like Southeast Asian countries, very limited number of rain gauges is available, and there is no implemented methodology for issuing effective warming of landslides yet. Correspondingly, satellite precipitation monitoring could be therefore a possible and promising solution for launching landslide quasi-real-time earlywarning system in those countries. It is due to the fact that TMPA (TRMM Multi-satellite Precipitation Analysis) can provides a globally calibration-based sequential scheme for combining precipitation estimates from multiple satellites, and gauge analyses where feasible, at fine scales (3-hourly with 0.25°x0.25° spatial resolution). It is available both after and in quasi-real time, calibrated by TRMM Combined Instrument and TRMM Microwave Imager precipitation product. However, validation of ground based rain gauge and TRMM satellite data in the vulnerable regions is still not yet operative. Snake-line/Critical-line and Soil Water Index (SWI) are used for issuing warning of landslide occurrence in Japan; whereas, Caine criterion is preferable in Europe and western nations. Herewith, it presents rainfall behavior which took place in Beichuan city (located on the 2008 Chinese Wenchuan earthquake fault), Hofu and Shobara cities in Japan where localized heavy rainfall attacked in 2009 and 2010, respectively, from TRMM 3B42RT correlated with ground based rain gauge data. The 1-day rainfall intensity and 15-day cumulative rainfall

Earlywarning, warning and alarm systems have gained popularity in recent years as cost-efficient measures for dangerous natural hazard processes such as floods, storms, rock and snow avalanches, debris flows, rock and ice falls, landslides, flash floods, glacier lake outburst floods, forest fires and even earthquakes. These systems can generate information before an event causes loss of property and life. In this way, they mainly mitigate the overall risk by reducing the presence probability of endangered objects. These systems are typically prototypes tailored to specific project needs. Despite their importance there is no recognised system classification. This contribution classifies warning and alarm systems into three classes: i) threshold systems, ii) expert systems and iii) model-based expert systems. The result is a generic classification, which takes the characteristics of the natural hazard process itself and the related monitoring possibilities into account. The choice of the monitoring parameters directly determines the system's lead time. The classification of 52 active systems moreover revealed typical system characteristics for each system class. i) Threshold systems monitor dynamic process parameters of ongoing events (e.g. water level of a debris flow) and incorporate minor lead times. They have a local geographical coverage and a predefined threshold determines if an alarm is automatically activated to warn endangered objects, authorities and system operators. ii) Expert systems monitor direct changes in the variable disposition (e.g crack opening before a rock avalanche) or trigger events (e.g. heavy rain) at a local scale before the main event starts and thus offer extended lead times. The final alarm decision incorporates human, model and organisational related factors. iii) Model-based expert systems monitor indirect changes in the variable disposition (e.g. snow temperature, height or solar radiation that influence the occurrence probability

Following the second Sahelian famine in 1984–1985, major investments were made to establish EarlyWarning Systems. These systems help to ensure that timely warnings and vulnerability information are available to decision makers to anticipate and avert food crises. In the recent crisis in the Horn of Africa, alarming levels of acute malnutrition were documented from March 2010, and by August 2010, an impending food crisis was forecast. Despite these measures, the situation remained unrecognised, and further deteriorated causing malnutrition levels to grow in severity and scope. By the time the United Nations officially declared famine on 20 July 2011, and the humanitarian community sluggishly went into response mode, levels of malnutrition and mortality exceeded catastrophic levels. At this time, an estimated 11 million people were in desperate and immediate need for food. With warnings of food crises in the Sahel, South Sudan, and forecast of the drought returning to the Horn, there is an immediate need to institutionalize change in the health response during humanitarian emergencies. Earlywarning systems are only effective if they trigger an early response. PMID:22745628

Following the second Sahelian famine in 1984-1985, major investments were made to establish EarlyWarning Systems. These systems help to ensure that timely warnings and vulnerability information are available to decision makers to anticipate and avert food crises. In the recent crisis in the Horn of Africa, alarming levels of acute malnutrition were documented from March 2010, and by August 2010, an impending food crisis was forecast. Despite these measures, the situation remained unrecognised, and further deteriorated causing malnutrition levels to grow in severity and scope. By the time the United Nations officially declared famine on 20 July 2011, and the humanitarian community sluggishly went into response mode, levels of malnutrition and mortality exceeded catastrophic levels. At this time, an estimated 11 million people were in desperate and immediate need for food. With warnings of food crises in the Sahel, South Sudan, and forecast of the drought returning to the Horn, there is an immediate need to institutionalize change in the health response during humanitarian emergencies. Earlywarning systems are only effective if they trigger an early response.

The tsunami disaster is a potential disaster in the territory of Indonesia. Indonesia is an archipelago country and close to the ocean deep. The tsunami occurred in Aceh province in 2004. Early prevention efforts have been carried out. One of them is making "tsunami buoy" which has been developed by BPPT. The tool puts sensors on the ocean floor near the coast to detect earthquakes on the ocean floor. Detection results are transmitted via satellite by a transmitter placed floating on the sea surface. The tool will cost billions of dollars for each system. Another constraint was the transmitter theft "tsunami buoy" in the absence of guard. In this study of the system has a transmission system using radio frequency and focused on coastal areas where costs are cheaper, so that it can be applied at many beaches in Indonesia are potentially affected by the tsunami. The monitoring system sends the detection results to the warning system using a radio frequency with a capability within 3 Km. Test results on the sub module sensor monitoring system generates an error of 0.63% was taken 10% showed a good quality sensing. The test results of data transmission from the transceiver of monitoring system to the receiver of warning system produces 100% successful delivery and reception of data. The test results on the whole system to function 100% properly.

The Global Geodetic Observing System has issued a Call for Participation to research scientists, geodetic research groups and national agencies in support of the implementation of the IUGG recommendation for a Global Navigation Satellite System (GNSS) Augmentation to Tsunami EarlyWarning Systems. The call seeks to establish a working group to be a catalyst and motivating force for the definition of requirements, identification of resources, and for the encouragement of international cooperation in the establishment, advancement, and utilization of GNSS for Tsunami EarlyWarning. During the past fifteen years the populations of the Indo-Pacific region experienced a series of mega-thrust earthquakes followed by devastating tsunamis that claimed nearly 300,000 lives. The future resiliency of the region will depend upon improvements to infrastructure and emergency response that will require very significant investments from the Indo-Pacific economies. The estimation of earthquake moment magnitude, source mechanism and the distribution of crustal deformation are critical to rapid tsunami warning. Geodetic research groups have demonstrated the use of GNSS data to estimate earthquake moment magnitude, source mechanism and the distribution of crustal deformation sufficient for the accurate and timely prediction of tsunamis generated by mega-thrust earthquakes. GNSS data have also been used to measure the formation and propagation of tsunamis via ionospheric disturbances acoustically coupled to the propagating surface waves; thereby providing a new technique to track tsunami propagation across ocean basins, opening the way for improving tsunami propagation models, and providing accurate warning to communities in the far field. These two new advancements can deliver timely and accurate tsunami warnings to coastal communities in the near and far field of mega-thrust earthquakes. This presentation will present the justification for and the details of the GGOS Call for

Tsunami warning in near-field conditions is a critical issue in the Mediterranean Sea since the most important tsunami sources are situated within tsunami wave travel times starting from about five minutes. The project NEARTOWARN (2012-2013) supported by the EU-DG ECHO contributed substantially to the development of new tools for the near-field tsunami earlywarning in the Mediterranean. One of the main achievements is the development of a local warning system in the test-site of Rhodes island (Rhodes EarlyWarning System for Earthquakes and Tsunamis - REWSET). The system is composed by three main subsystems: (1) a network of eight seismic earlywarning devices installed in four different localities of the island, one in the civil protection, another in the Fire Brigade and another two in municipality buildings; (2) two radar-type (ultrasonic) tide-gauges installed in the eastern coastal zine of the island which was selected since research on the historical earthquake and tsunami activity has indicated that the most important, near-field tsunami sources are situated offshore to the east of Rhodes; (3) a crisis Geographic Management System (GMS), which is a web-based and GIS-based application incorporating a variety of thematic maps and other information types. The seismic earlywarning devices activate by strong (magnitude around 6 or more) earthquakes occurring at distances up to about 100 km from Rhodes, thus providing immediate mobilization of the civil protection. The tide-gauges transmit sea level data, while during the crisis the GMS supports decisions to be made by civil protection. In the near future it is planned the REWSET system to be integrated with national and international systems. REWSET is a prototype which certainly could be developed in other coastal areas of the Mediterranean and beyond.

For 20 years the South Iceland Seismic Zone (SISZ) was a test site for multinational earthquake prediction research, partly bridging the gap between laboratory tests samples, and the huge transform zones of the Earth. The approach was to explore the physics of processes leading up to large earthquakes. The book Advances in Earthquake Prediction, Research and Risk Mitigation, by R. Stefansson (2011), published by Springer/PRAXIS, and an article in the August issue of the BSSA by Stefansson, M. Bonafede and G. Gudmundsson (2011) contain a good overview of the findings, and more references, as well as examples of partially successful long and short term warnings based on such an approach. Significant findings are: Earthquakes that occurred hundreds of years ago left scars in the crust, expressed in volumes of heterogeneity that demonstrate the size of their faults. Rheology and stress heterogeneity within these volumes are significantly variable in time and space. Crustal processes in and near such faults may be observed by microearthquake information decades before the sudden onset of a new large earthquake. High pressure fluids of mantle origin may in response to strain, especially near plate boundaries, migrate upward into the brittle/elastic crust to play a significant role in modifying crustal conditions on a long and short term. Preparatory processes of various earthquakes can not be expected to be the same. We learn about an impending earthquake by observing long term preparatory processes at the fault, finding a constitutive relationship that governs the processes, and then extrapolating that relationship into near space and future. This is a deterministic approach in earthquake prediction research. Such extrapolations contain many uncertainties. However the long time pattern of observations of the pre-earthquake fault process will help us to put probability constraints on our extrapolations and our warnings. The approach described is different from the usual

A climate 'tipping point' occurs when a small change in forcing triggers a strongly nonlinear response in the internal dynamics of part of the climate system, qualitatively changing its future state. Human-induced climate change could push several large-scale 'tipping elements' past a tipping point. Candidates include irreversible melt of the Greenland ice sheet, dieback of the Amazon rainforest and shift of the West African monsoon. Recent assessments give an increased probability of future tipping events, and the corresponding impacts are estimated to be large, making them significant risks. Recent work shows that earlywarning of an approaching climate tipping point is possible in principle, and could have considerable value in reducing the risk that they pose.

EarlyEarthquakeWarning systems could reduce loss of lives and other economic impact resulted from natural disaster or man-made calamity. Current systems could be further enhanced by neutral network method. A 3 layer neural network model combined with onsite method was deployed in this paper to improve the recognition time and detection time for large scale earthquakes.The 3 layer neutral network earlyearthquakewarning model adopted the vector feature design for sample events happened within 150 km radius of the epicenters. Dataset used in this paper contained both destructive events and small scale events. All the data was extracted from IRIS database to properly train the model. In the training process, backpropagation algorithm was used to adjust the weight matrices and bias matrices during each iteration. The information in all three channels of the seismometers served as the source in this model. Through designed tests, it was indicated that this model could identify approximately 90 percent of the events' scale correctly. And the early detection could provide informative evidence for public authorities to make further decisions. This indicated that neutral network model could have the potential to strengthen current earlywarning system, since the onsite method may greatly reduce the responding time and save more lives in such disasters.

Abstract Problem On 6 February 2013, an 8.0 magnitude earthquake generated a tsunami that struck the Santa Cruz Islands, Solomon Islands, killing 10 people and displacing over 4700. Approach A post-disaster assessment of the risk of epidemic disease transmission recommended the implementation of an earlywarning alert and response network (EWARN) to rapidly detect, assess and respond to potential outbreaks in the aftermath of the tsunami. Local setting Almost 40% of the Santa Cruz Islands’ population were displaced by the disaster, and living in cramped temporary camps with poor or absent sanitation facilities and insufficient access to clean water. There was no earlywarning disease surveillance system. Relevant changes By 25 February, an EWARN was operational in five health facilities that served 90% of the displaced population. Eight priority diseases or syndromes were reported weekly; unexpected health events were reported immediately. Between 25 February and 19 May, 1177 target diseases or syndrome cases were reported. Seven alerts were investigated. No sustained transmission or epidemics were identified. Reporting compliance was 85%. The EWARN was then transitioned to the routine four-syndrome earlywarning disease surveillance system. Lesson learnt It was necessary to conduct a detailed assessment to evaluate the risk and potential impact of serious infectious disease outbreaks, to assess whether and how enhanced earlywarning disease surveillance should be implemented. Local capacities and available resources should be considered in planning EWARN implementation. An EWARN can be an opportunity to establish or strengthen earlywarning disease surveillance capabilities. PMID:25378746

On 6 February 2013, an 8.0 magnitude earthquake generated a tsunami that struck the Santa Cruz Islands, Solomon Islands, killing 10 people and displacing over 4700. A post-disaster assessment of the risk of epidemic disease transmission recommended the implementation of an earlywarning alert and response network (EWARN) to rapidly detect, assess and respond to potential outbreaks in the aftermath of the tsunami. Almost 40% of the Santa Cruz Islands' population were displaced by the disaster, and living in cramped temporary camps with poor or absent sanitation facilities and insufficient access to clean water. There was no earlywarning disease surveillance system. By 25 February, an EWARN was operational in five health facilities that served 90% of the displaced population. Eight priority diseases or syndromes were reported weekly; unexpected health events were reported immediately. Between 25 February and 19 May, 1177 target diseases or syndrome cases were reported. Seven alerts were investigated. No sustained transmission or epidemics were identified. Reporting compliance was 85%. The EWARN was then transitioned to the routine four-syndrome earlywarning disease surveillance system. It was necessary to conduct a detailed assessment to evaluate the risk and potential impact of serious infectious disease outbreaks, to assess whether and how enhanced earlywarning disease surveillance should be implemented. Local capacities and available resources should be considered in planning EWARN implementation. An EWARN can be an opportunity to establish or strengthen earlywarning disease surveillance capabilities.

Among the many options available to mitigate landslide risk, earlywarning systems may be used where, in specific circumstances, the risk to life increases above tolerable levels. A coherent framework to classify and analyse landslide earlywarning systems (LEWS) is herein presented. Once the objectives of an earlywarning strategy are defined depending on the scale of analysis and the type of landslides to address, the process of designing and managing a LEWS should synergically employ technical and social skills. A classification scheme for the main components of LEWSs is proposed for weather-induced landslides. The scheme is based on a clear distinction among: i) the landslide model, i.e. a functional relationship between weather characteristics and landslide events considering the geotechnical, geomorphological and hydro-geological characterization of the area as well as an adequate monitoring strategy; ii) the warning model, i.e. the landslide model plus procedures to define the warning events and to issue the warnings; iii) the warning system, i.e. the warning model plus warning dissemination procedures, communication and education tools, strategies for community involvement and emergency plans. Each component of a LEWS is related to a number of actors involved with their deployment, operational activities and management. For instance, communication and education, community involvement and emergency plans are all significantly influenced by people's risk perception and by operational aspects system managers need to address in cooperation with scientists.

Operational earlywarning platform for extreme meteorological events Most natural disasters are related to extreme weather events (e.g. typhoons); weather conditions, however, are also highly relevant for humanitarian and disaster relief operations during and after other natural disaster like earthquakes. The internet service "Wettergefahren-Frühwarnung" (WF) provides various information on extreme weather events, especially when these events are associated with a high potential for large damage. The main focus of the platform is on Central Europe, but major events are also monitored worldwide on a daily routine. WF provides high-resolution forecast maps for many weather parameters which allow detailed and reliable predictions about weather conditions during the next days in the affected areas. The WF service became operational in February 2004 and is part of the Center for Disaster Management and Risk Reduction Technology (CEDIM) since 2007. At the end of 2011, CEDIM embarked a new type of interdisciplinary disaster research termed as forensic disaster analysis (FDA) in near real time. In case of an imminent extreme weather event WF plays an important role in CEDIM's FDA group. It provides early and precise information which are always available and updated several times during a day and gives advice and assists with articles and reports on extreme events.

In September 1998, President Clinton and President Yeltsin issued a statement that our two countries would develop a system to share data from our respective earlywarning systems. The purpose of the initiative is to further reduce the risk of ballistic missile launches occurring in response to a misunderstanding about the data from such systems. The proposal includes a permanent center for sharing such data, located in Moscow, separate from but communicating with the strategic command-and-control centers of each country. It also includes development of a system of pre-launch notifications, which is expected to eventually provide notification of a broad class of launches, on a voluntary basis, including launches by all the countries that engage in missile and space activities. The status, progress, and prognosis for the work will be discussed. The presentation will address the experience gained from the operation of the Center for Y2K Strategic Stability in Colorado Springs (12/99 - 01/00), which tested many of our ideas for a joint center sharing both pre- launch and sensor data on worldwide launches. In addition, the potential of the initiative -- the first arms control effort involving active and continuing U.S.-Russian joint operations -- to provide a model for future arms control opportunities will be discussed.

The EarlyWarning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicators models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U.S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.

Recent theoretical and experimental studies explicitly demonstrated the ability of space technologies to identify and monitor the specific variations at near-earth space plasma, atmosphere and ground surface associated with approaching severe earthquakes (named as earthquake precursors) appearing several days (from 1 to 5) before the seismic shock over the seismically active areas. Several countries and private companies are in the stage of preparation (or already launched) the dedicated spacecrafts for monitoring of the earthquake precursors from space and for short-term earthquake prediction. The present paper intends to outline the optimal algorithm for creation of the space-borne system for the earthquake precursors monitoring and for short-term earthquake prediction. It takes into account the following considerations: Selection of the precursors in the terms of priority, taking into account their statistical and physical parameters Configuration of the spacecraft payload Configuration of the satellite constellation (orbit selection, satellite distribution, operation schedule) Proposal of different options (cheap microsatellite or comprehensive multisatellite constellation) Taking into account that the most promising are the ionospheric precursors of earthquakes, the special attention will be devoted to the radiophysical techniques of the ionosphere monitoring. The advantages and disadvantages of such technologies as vertical sounding, in-situ probes, ionosphere tomography, GPS TEC and GPS MET technologies will be considered.

Recent theoretical and experimental studies explicitly demonstrated the ability of space technologies to identify and monitor the specific variations at near-earth space plasma, atmosphere and ground surface associated with approaching severe earthquakes (named as earthquake precursors) which appear several days (from 1 to 5) before the seismic shock over the seismically active areas. Several countries and private companies are in the stage of preparation (or already launched) the dedicated spacecrafts for monitoring of the earthquake precursors from space and for short-term earthquake prediction. The present paper intends to outline the optimal algorithm for creation of the space-borne system for the earthquake precursors monitoring and for short-term earthquake prediction. It takes into account the following: Selection of the precursors in the terms of priority, considering their statistical and physical parameters.Configuration of the spacecraft payload.Configuration of the satellite constellation (orbit selection, satellite distribution, operation schedule).Different options of the satellite systems (cheap microsatellite or comprehensive multisatellite constellation). Taking into account that the most promising are the ionospheric precursors of earthquakes, the special attention is devoted to the radiophysical techniques of the ionosphere monitoring. The advantages and disadvantages of such technologies as vertical sounding, in-situ probes, ionosphere tomography, GPS TEC and GPS MET technologies are considered.

To identify the earlywarning signs of severe preeclampsia (SPE). A case-control (1:2) observational study was conducted. Forty-seven pregnant women with SPE, who attended the prenatal clinics of Peking University Third Hospital regularly from Jan. 2002 to Dec. 2007, were selected as the study group, including 12 early onset and 35 late onset ones. The control group consisted of 94 healthy singleton pregnant women at the same period. Clinical data were collected and analyzed. (1) The basal body mass index (BMI) showed no difference between the study and control group [(23.27 +/- 4.31) kg/m(2) vs (21.52 +/- 3.09) kg/m(2), P > 0.05]. (2) The net increase of BMI in the study group before the onset of SPE was higher than that in the control [(5.60 +/- 2.17) kg/m(2) vs (4.85 +/- 1.52) kg/m(2), P < 0.05] and the increase of BMI per week was also higher [(0.74 +/- 0.41) kg/(m(2).w)(-1) vs (0.23 +/- 0.18) kg/(m(2).w)(-1), P < 0.01]. The sensitivity and specificity of BMI increase per week in predicting SPE was 84% and 81% at a cut-off value of 0.39 kg/(m(2).w)(-1), respectively, and 79% and 91% at 0.41 kg/(m(2).w)(-1) correspondingly. (3) During the third trimester and before the onset of SPE, the weight gain per week in the study group was higher than that of the control [(0.93 +/- 0.70) kg vs (0.63 +/- 0.20) kg, P < 0.01]. Significant difference was also found in the net weight gain between the two groups (P < 0.01), but not in the percentage of women with excessive weight gain (> 0.50 kg/w) [60% (25/42) in the study group vs 63% (53/84) in the control group, P > 0.05]. (4) Higher percentage of women experienced pre-hypertension in the study group than in the controls [17% (8/47) vs 5% (5/94), P < 0.01]. (5) In the study group, 53% (25/47) of the women had edema before SPE onset, but the figure dropped to 18% (17/94) in the controls (P < 0.01). (6) Eight women in the study group and one in the control group suffered from hypoproteinemia before SPE onset with the average

It is imperative that losses expected due to future earthquakes be estimated. Officials and the public need to be aware of what disaster is likely in store for them in order to reduce the fatalities and efficiently help the injured. Scenarios for earthquake parameters can be constructed to a reasonable accuracy in highly active earthquake belts, based on knowledge of seismotectonics and history. Because of the inherent uncertainties of loss estimates however, it would be desirable that more than one group calculate an estimate for the same area. By discussing these estimates, one may find a consensus of the range of the potential disasters and persuade officials and residents of the reality of the earthquake threat. To model a scenario and estimate earthquake losses requires data sets that are sufficiently accurate of the number of people present, the built environment, and if possible the transmission of seismic waves. As examples we use loss estimates for possible repeats of historic earthquakes in Greece that occurred between -464 and 700. We model future large Greek earthquakes as having M6.8 and rupture lengths of 60 km. In four locations where historic earthquakes with serious losses have occurred, we estimate that 1,000 to 1,500 people might perish, with an additional factor of four people injured. Defining the area of influence of these earthquakes as that with shaking intensities larger and equal to V, we estimate that 1.0 to 2.2 million people in about 2,000 settlements may be affected. We calibrate the QLARM tool for calculating intensities and losses in Greece, using the M6, 1999 Athens earthquake and matching the isoseismal information for six earthquakes, which occurred in Greece during the last 140 years. Comparing fatality numbers that would occur theoretically today with the numbers reported, and correcting for the increase in population, we estimate that the improvement of the building stock has reduced the mortality and injury rate in Greek

The objective of the Tsunami calculations is the estimation of the impact of waves caused by large seismic events on the coasts and the determination of potential inundation areas. In the case of EarlyWarning Systems, i.e. systems that should allow to anticipate the possible effects and give the possibility to react consequently (i.e. issue evacuation of areas at risk), this must be done in very short time (minutes) to be effective. In reality, the above estimation includes several uncertainty factors which make the prediction extremely difficult. The quality of the very first estimations of the seismic parameters is not very precise: the uncertainty in the determination of the seismic components (location, magnitude and depth) decreases with time because as time passes it is possible to use more and more seismic signals and the event characterization becomes more precise. On the other hand other parameters that are necessary to establish for the performance of a calculation (i.e. fault mechanism) are difficult to estimate accurately also after hours (and in some cases remain unknown) and therefore this uncertainty remains in the estimated impact evaluations; when a quick tsunami calculation is necessary (earlywarning systems) the possibility to include any possible future variation of the conditions to establish the "worst case scenario" is particularly important. The consequence is that the number of uncertain parameters is so large that it is not easy to assess the relative importance of each of them and their effect on the predicted results. In general the complexity of system computer codes is generated by the multitude of different models which are assembled into a single program to give the global response for a particular phenomenon. Each of these model has associated a determined uncertainty coming from the application of that model to single cases and/or separated effect test cases. The difficulty in the prediction of a Tsunami calculation response is

This analysis was performed to assist the National Highway Traffic Safety Administration (NHTSA) in identifying components and systems to be included in earlywarning reporting (EWR) categories that would be based upon historical safety-related recal...

The system construction of urban flood control and disaster reduction in China is facing pressure and challenge from new urban water disaster. Under the circumstances that it is difficult to build high standards of flood protection engineering measures in urban areas, it is particularly important to carry out urban flood earlywarning. In Jinan City, a representative inland area, based on the index system of earlywarning of flood in Jinan urban area, the method of fuzzy comprehensive evaluation was adopted to evaluate the level of earlywarning. Based on the cumulative rainfall of 3 hours, the CAflood simulation results based on cellular automaton model of urban flooding were used as evaluation indexes to realize the accuracy and integration of urban flood control earlywarning.

In almost all currently operating earthquakeearlywarning (EEW) systems, presently available seismic data are used to predict future shaking. In most cases, location and magnitude are estimated. We are developing an algorithm to test the goodness of that prediction in real time. We monitor envelopes of acceleration, velocity, and displacement; if they deviate significantly from the envelope predicted by Cua's envelope gmpe's then we declare an overfit (perhaps false alarm) or an underfit (possibly a larger event has just occurred). This algorithm is designed to provide a robust measure and to work as quickly as possible in real-time. We monitor the logarithm of the ratio between the envelopes of the ongoing observed event and the envelopes derived from the predicted envelopes of channels of ground motion of the Virtual Seismologist (VS) (Cua, G. and Heaton, T.). Then, we recursively filter this result with a simple running median (de-spiking operator) to minimize the effect of one single high value. Depending on the result of the filtered value we make a decision such as if this value is large enough (e.g., >1), then we would declare, 'that a larger event is in progress', or similarly if this value is small enough (e.g.,

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of "decomposition and ensemble" and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality earlywarning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.

Earlywarning systems are a tool with which to minimize risks posed by climate related hazards. Although great strides have been made in developing earlywarning systems most deal with one hazard, only provide short-term warnings and do not reach the most vulnerable. This presentation will review research results of the United Nations Environment Programme's CLIM-WARN project. The project seeks to identify how governments can better communicate risks by designing multi-hazard earlywarning systems that deliver actionable warnings across timescales. Household surveys and focus group discussions were conducted in 36 communities in Kenya, Ghana and Burkina Faso in order to identify relevant climate related hazards, current response strategies and earlywarning needs. Preliminary results show significant variability in both risks and needs within and between countries. For instance, floods are more frequent in rural western parts of Kenya. Droughts are frequent in the north while populations in urban areas face a range of hazards - floods, droughts, disease outbreaks - that sometimes occur simultaneously. The majority of the rural population, especially women, the disabled and the elderly, do not have access to modern media such as radio, television, or internet. While 55% of rural populace never watches television, 64% of urban respondents watch television on a daily basis. Communities have different concepts of how to design warning systems. It will be a challenge for national governments to create systems that accommodate such diversity yet provide standard quality of service to all. There is a need for flexible and forward-looking earlywarning systems that deliver broader information about risks. Information disseminated through the system could not only include details of hazards, but also long-term adaptation options, general education, and health information, thus increasingly both capabilities and response options.

Portugal mainland is located near the border between the Eurasian and Nubian plates, whose interaction is the main responsible for a significant seismic activity in the area, with historical occurrence of several catastrophic events (e.g. Lisbon 1755 earthquake [Mag 8.7]), most of which haviguilhng epicenter rise in submerged area, located in the Cadiz Gulf and Southwest of San Vincent Cape. EarlyWarning Systems (EEWS) is presently a very effective concept to be applied in the mitigation of the effects caused by large earthquakes. For the mentioned area a feasibility study of a EEWS was made in the ALERT-ES project. It was found that the system could be effective to protect cities and infrastructures located at larger distances (ex: Lisbon) from the areas, located south and southwest of PT mainland, where the larger earthquakes are expected to be originated. Considering the use of a new strong-motion network recently implemented in the south of PT mainland, we concluded that the lead-times could be improved. We opted by the implementation of the well known computational platform PRESTO. In the adaptation of the mentioned platform to the local reality one of the challenges was the computation of fast moment magnitude estimates, because regional attenuation must be properly considered, and a specific study was made on this issue. The several simulations that were performed showed a reasonably good performance of the system, both on magnitude evaluation and epicentre location. However we also noted that the problems in the acquisition instruments are a very important source of disturbance in the performance of the EEWS, pointing to a need of a very accurate quality control of the strong-motion network. Considering end-users, we are also developing specific software for intensity estimation at the target places and to trigger visual and audio alerts in accordance to the expected level of shaking. This work is supported by the EU project TSUMAPS-NEAM, Agreement Number

Indonesia has Indonesian Tsunami EarlyWarning System (Ina-TEWS) since 2008. The Ina-TEWS has used automatic processing on hypocenter; Mwp, Mw (mB) and Mj. If earthquake occurred in Ocean, depth < 70 km and magnitude > 7, then Ina-TEWS announce earlywarning that the earthquake can generate tsunami. However, the announcement of the Ina-TEWS is still not accuracy. Purposes of this research are to estimate earthquake rupture duration of large Indonesia earthquakes that occurred in Indian Ocean, Java, Timor sea, Banda sea, Arafura sea and Pasific ocean. We analyzed at least 330 vertical seismogram recorded by IRIS-DMC network using a directmore » procedure for rapid assessment of earthquake tsunami potential using simple measures on P-wave vertical seismograms on the velocity records, and the likelihood that the high-frequency, apparent rupture duration, T{sub dur}. T{sub dur} can be related to the critical parameters rupture length (L), depth (z), and shear modulus ({mu}) while T{sub dur} may be related to wide (W), slip (D), z or {mu}. Our analysis shows that the rupture duration has a stronger influence to generate tsunami than Mw and depth. The rupture duration gives more information on tsunami impact, Mo/{mu}, depth and size than Mw and other currently used discriminants. We show more information which known from the rupture durations. The longer rupture duration, the shallower source of the earthquake. For rupture duration greater than 50 s, the depth less than 50 km, Mw greater than 7, the longer rupture length, because T{sub dur} is proportional L and greater Mo/{mu}. Because Mo/{mu} is proportional L. So, with rupture duration information can be known information of the four parameters. We also suggest that tsunami potential is not directly related to the faulting type of source and for events that have rupture duration greater than 50 s, the earthquakes generated tsunami. With available real-time seismogram data, rapid calculation, rupture duration

Improved earthquake models, better tsunami modeling and warning capabilities, and a review of nuclear power plant safety are all greatly needed following the 11 March Tohoku earthquake and tsunami, according to scientists at the European Geosciences Union's (EGU) General Assembly, held 3-8 April in Vienna, Austria. EGU quickly organized a morning session of oral presentations and an afternoon panel discussion less than 1 month after the earthquake and the tsunami and the resulting crisis at Japan's Fukushima nuclear power plant, which has now been identified as having reached the same level of severity as the 1986 Chernobyl disaster. Many of the scientists at the EGU sessions expressed concern about the inability to have anticipated the size of the earthquake and the resulting tsunami, which appears likely to have caused most of the fatalities and damage, including damage to the nuclear plant.

PRESTo (PRobabilistic and Evolutionary earlywarning SysTem) is a software platform for EarthquakeEarlyWarning that integrates algorithms for real-time earthquake location, magnitude estimation and damage assessment into a highly configurable and easily portable package. In its regional configuration, the software processes, in real-time, the 3-component acceleration data streams coming from seismic stations, for P-waves arrival detection and, in the case a quite large event is occurring, can promptly performs event detection and location, magnitude estimation and peak ground-motion prediction at target sites. The regional approach has been integrated with a threshold-based earlywarning method that allows, in the very first seconds after a moderate-to-large earthquake, to identify the most Probable Damaged Zone starting from the real-time measurement at near-source stations located at increasing distances from the earthquake epicenter, of the peak displacement (Pd) and predominant period of P-waves (τc), over a few-second long window after the P-wave arrival. Thus, each recording site independently provides an evolutionary alert level, according to the Pd and τc it measured, through a decisional table. Since 2009, PRESTo has been under continuous real-time testing using data streaming from the Iripinia Seismic Network (Southern Italy) and has produced a bulletin of some hundreds low magnitude events, including all the M≥2.5 earthquakes occurred in that period in Irpinia. Recently, PRESTo has been also implemented at the accelerometric network and broad-band networks in South Korea and in Romania, and off-line tested in Iberian Peninsula, in Turkey, in Israel, and in Japan. The feasibility of an EarlyWarning System at national scale, is currently under testing by studying the performances of the PRESTo platform for the Italian Accelerometric Network. Moreover, PRESTo is under experimentation in order to provide alert in a high-school located in the

Flood forecasting and earlywarning has continued to stride ahead in strengthening the preparedness phases of disaster risk management, saving lives and property and reducing the overall impact of severe flood events. For example, continental and global scale flood forecasting systems such as the European Flood Awareness System and the Global Flood Awareness System provide early information about upcoming floods in real time to various decisionmakers. Studies have found that there are monetary benefits to implementing these early flood warning systems, and with the science also in place to provide evidence of benefit and hydrometeorological institutional outlooks warming to the use of probabilistic forecasts, the uptake over the last decade has been rapid and sustained. However, there are many further challenges that lie ahead to improve the science supporting flood earlywarning and to ensure that appropriate decisions are made to maximise flood preparedness.

Bucharest, with a population of approximately 2 million people, has suffered damage from earthquakes in the Vrancea seismic zone, which is located about 170 km from Bucharest, at a depth of 80-200 km. Consequently, an earthquakeearlywarning system (Bucharest Rapid earthquakeEarlyWarning System or BREWS) was constructed to provide some warning about impending shaking from large earthquakes in the Vrancea zone. In order to provide quick estimates of magnitude, seismic moment was first determined from P-waves and then a moment magnitude was determined from the moment. However, this magnitude may not be consistent with previous estimates of magnitude from the Romanian Seismic Network. This paper introduces the algorithm using P-wave spectral levels and compares them with catalog estimates. The testing procedure used waveforms from about 90 events with catalog magnitudes from 3.5 to 5.4. Corrections to the P-wave determined magnitudes according to dominant intermediate depth events mechanism were tested for November 22, 2014, M5.6 and October 17, M6 events. The corrections worked well, but unveiled overestimation of the average magnitude result of about 0.2 magnitude unit in the case of shallow depth event ( H < 60 km). The P-wave spectral approach allows for the relatively fast estimates of magnitude for use in BREWS. The average correction taking into account the most common focal mechanism for radiation pattern coefficient may lead to overestimation of the magnitude for shallow events of about 0.2 magnitude unit. However, in case of events of intermediate depth of M6 the resulting M w is underestimated at about 0.1-0.2. We conclude that our P-wave spectral approach is sufficiently robust for the needs of BREWS for both shallow and intermediate depth events.

School attendance can be an early indicator that something is going wrong with a student. Gathering, analyzing, and acting on attendance information is a first step toward school improvement. Meanwhile, the majority of the states are moving to build and enhance what are called "earlywarning systems," intended to flag at-risk students during their…

Over the last 20 years, natural disasters have killed nearly 3 million people and disrupted the lives of over 800 million others. In 2 years there were more than 50 serious natural disasters, including landslides in Italy, France, and Colombia; a typhoon in Korea; wildfires in China and the United States; a windstorm in England; grasshopper plagues in Africa's horn and the Sahel; tornadoes in Canada; devastating earthquakes in Soviet Armenia and Tadzhikstand; infestations in Africa; landslides in Brazil; and tornadoes in the United States

Debris flows are natural disasters that frequently occur in mountainous areas, usually accompanied by serious loss of lives and properties. One of the most commonly used approaches to mitigate the risk associated with debris flows is the implementation of earlywarning systems based on well-calibrated rainfall thresholds. However, many mountainous areas have little data regarding rainfall and hazards, especially in debris-flow-forming regions. Therefore, the traditional statistical analysis method that determines the empirical relationship between rainstorms and debris flow events cannot be effectively used to calculate reliable rainfall thresholds in these areas. After the severe Wenchuan earthquake, there were plenty of deposits deposited in the gullies, which resulted in several debris flow events. The triggering rainfall threshold has decreased obviously. To get a reliable and accurate rainfall threshold and improve the accuracy of debris flow earlywarning, this paper developed a quantitative method, which is suitable for debris flow triggering mechanisms in meizoseismal areas, to identify rainfall threshold for debris flow earlywarning in areas with a scarcity of data based on the initiation mechanism of hydraulic-driven debris flow. First, we studied the characteristics of the study area, including meteorology, hydrology, topography and physical characteristics of the loose solid materials. Then, the rainfall threshold was calculated by the initiation mechanism of the hydraulic debris flow. The comparison with other models and with alternate configurations demonstrates that the proposed rainfall threshold curve is a function of the antecedent precipitation index (API) and 1 h rainfall. To test the proposed method, we selected the Guojuanyan gully, a typical debris flow valley that during the 2008-2013 period experienced several debris flow events, located in the meizoseismal areas of the Wenchuan earthquake, as a case study. The comparison with other

Effective disaster risk management relies on science based solutions to close the gap between prevention and preparedness measures. The outcome of consultations on the UNIDSR post-2015 framework for disaster risk reduction highlight the need for cross-border earlywarning systems to strengthen the preparedness phases of disaster risk management in order to save people's lives and property and reduce the overall impact of severe events. In particular, continental and global scale flood forecasting systems provide vital information to various decision makers with which earlywarnings of floods can be made. Here the potential monetary benefits of early flood warnings using the example of the European Flood Awareness System (EFAS) are calculated based on pan-European Flood damage data and calculations of potential flood damage reductions. The benefits are of the order of 400 Euro for every 1 Euro invested. Because of the uncertainties which accompany the calculation, a large sensitivity analysis is performed in order to develop an envelope of possible financial benefits. Current EFAS system skill is compared against perfect forecasts to demonstrate the importance of further improving the skill of the forecasts. Improving the response to warnings is also essential in reaping the benefits of flood earlywarnings.

Flood earlywarning systems play a major role in the disaster risk reduction paradigm as cost-effective methods to mitigate flood disaster damage. The connections and feedbacks between the hydrological and social spheres of earlywarning systems are increasingly being considered as key aspects for successful flood mitigation. The behavior of the public and first responders during flood situations, determined by their preparedness, is heavily influenced by many behavioral traits such as perceived benefits, risk awareness, or even denial. In this study, we use the recency of flood experiences as a proxy for social preparedness to assess its impact on the efficiency of flood earlywarning systems through a simple stylized model and implemented this model using a simple mathematical description. The main findings, which are based on synthetic data, point to the importance of social preparedness for flood loss mitigation, especially in circumstances where the technical forecasting and warning capabilities are limited. Furthermore, we found that efforts to promote and preserve social preparedness may help to reduce disaster-induced losses by almost one half. The findings provide important insights into the role of social preparedness that may help guide decision-making in the field of flood earlywarning systems.

All complex development projects experience delays and corresponding backlogs of their project control milestones during their acquisition lifecycles. NASA Goddard Space Flight Center (GSFC) Flight Projects Directorate (FPD) teamed with The Aerospace Corporation (Aerospace) to develop a collection of EarlyWarning Look Ahead metrics that would provide GSFC leadership with some independent indication of the programmatic health of GSFC flight projects. As part of the collection of EarlyWarning Look Ahead metrics, the Percent Milestone Backlog metric is particularly revealing, and has utility as a stand-alone execution performance monitoring tool. This paper describes the purpose, development methodology, and utility of the Percent Milestone Backlog metric. The other four EarlyWarning Look Ahead metrics are also briefly discussed. Finally, an example of the use of the Percent Milestone Backlog metric in providing actionable insight is described, along with examples of its potential use in other commodities.

The ability to reliably predict critical transitions in dynamical systems is a long-standing goal of diverse scientific communities. Previous work focused on earlywarning signals related to local bifurcations (critical slowing down) and nonbifurcation-type transitions. We extend this toolbox and report on a characteristic scaling behavior (critical attractor growth) which is indicative of an impending global bifurcation, an interior crisis in excitable systems. We demonstrate our earlywarning signal in a conceptual climate model as well as in a model of coupled neurons known to exhibit extreme events. We observed critical attractor growth prior to interior crises of chaotic as well as strange-nonchaotic attractors. These observations promise to extend the classes of transitions that can be predicted via earlywarning signals.

The natural disaster of the Boxing Day Tsunami of 2004 was followed by an information catastrophe. Crucial earlywarning information could not be delivered to the communities under imminent threat, resulting in over 240,000 casualties in 14 countries. This tragedy sparked the development of a new generation of integrated modular Tsunami EarlyWarning Systems (TEWS). While significant advances were accomplished in the past years, recent events, like the Chile 2010 and the Tohoku 2011 tsunami demonstrate that the key technical challenge for Tsunami EarlyWarning research on the supranational scale still lies in the timely issuing of status information and reliable earlywarning messages in a proven workflow. A second challenge stems from the main objective of the Intergovernmental Oceanographic Commission of UNESCO (IOC) Tsunami Programme, the integration of national TEWS towards ocean-wide networks: Each of the increasing number of integrated Tsunami EarlyWarning Centres has to cope with the continuing evolution of sensors, hardware and software while having to maintain reliable inter-center information exchange services. To avoid future information catastrophes, the performance of all components, ranging from individual sensors, to Warning Centers within their particular end-to-end Warning System Environments, and up to federated Systems of Tsunami Warning Systems has to be regularly validated against defined criteria. Since 2004, GFZ German Research Centre for Geosciences (GFZ) has built up expertise in the field of TEWS. Within GFZ, the Centre for GeoInformation Technology (CeGIT) has focused its work on the geoinformatics aspects of TEWS in two projects already, being the German Indonesian Tsunami EarlyWarning System (GITEWS) and the Distant EarlyWarning System (DEWS). This activity is continued in the TRIDEC project (Collaborative, Complex, and Critical Decision Processes in Evolving Crises) funded under the European Union's seventh Framework Programme (FP7

Every year a few damaging earthquakes occur in the European-Mediterranean region. It is therefore indispensable to operate a real-time warning system in order to pro- vide rapidly reliable estimates of the location, depth and magnitude of these seismic events. In order to provide this information in a timely manner both to the scientific community and to the European and national authorities dealing with natural hazards and relief organisation, the European-Mediterranean Seismological Centre (EMSC) has federated a network of seismic networks exchanging their data in quasi real-time. Today, thanks to the Internet, the EMSC receives real-time information about earth- quakes from about thirty seismological institutes. As soon as data reach the EMSC, they are displayed on the EMSC Web pages (www.emsc-csem.org). A seismic alert is generated for any potentially damaging earthquake in the European-Mediterranean re- gion, potentially damaging earthquakes being defined as seismic events of magnitude 5 or more. The warning system automatically issues a message to the duty seismolo- gist mobile phone and pager. The seismologist log in to the EMSC computers using a laptop PC and relocates the earthquake by processing together all information pro- vided by the networks. The new location and magnitude are then send, by fax, telex, and email, within one hour following the earthquake occurrence, to national and inter- national organisations whose activities are related to seismic risks, and to the EMSC members. The EMSC rapid warning system has been fully operational for more than 4 years. Its distributed architecture has proved to be an efficient and reliable way for the monitoring of potentially damaging earthquakes. Furthermore, if a major problem disrupts the operational system more than 30 minutes, the duty is taken, over either by the Instituto Geografico National in Spain or by the Istituto Nazionale di Geofisica in Italy. The EMSC operational centre, located at the

The German-Indonesian Tsunami EarlyWarning System (GITEWS) for the Indian Ocean region has gone into operation in Indonesia in November 2008. The system includes a seismological network, together with GPS stations and a network of GPS buoys additionally equipped with ocean bottom pressure sensors and a tide gauge network. The different sensor systems have, for the most part, been installed and now deliver respective data either online or interactively upon request to the Warning Centre in Jakarta. Before 2011, however, the different components requires further optimization and fine tuning, local personnel needs to be trained and eventual problems in the daily operation have to be dealt with. Furthermore a company will be founded in the near future, which will guarantee a sustainable maintenance and operation of the system. This concludes the transfer from a temporarily project into a permanent service. This system established in Indonesia differs from other Tsunami Warning Systems through its application of modern scientific methods and technologies. New procedures for the fast and reliable determination of strong earthquakes, deformation monitoring by GPS, the modeling of tsunamis and the assessment of the situation have been implemented in the Warning System architecture. In particular, the direct incorporation of different sensors provides broad information already at the early stages of EarlyWarning thus resulting in a stable system and minimizing breakdowns and false alarms. The warning system is designed in an open and modular structure based on the most recent developments and standards of information technology. Therefore, the system can easily integrate additional sensor components to be used for other multi-hazard purposes e.g. meteorological and hydrological events. Up to now the German project group is cooperating in the Indian Ocean region with Sri Lanka, the Maldives, Iran, Yemen, Tanzania and Kenya to set up the equipment primarily for

The largest Pacific basin earthquake in 47 years, and also the largest magnitude earthquake since the Sumatra 2004 earthquake, struck off of the east coast of the Tohoku region of Honshu, Japan at 5:46 UTC on 11 March 2011. The Tohoku earthquake (Mw 9.0) generated a massive tsunami with runups of up to 40m along the Tohoku coast. The tsunami waves crossed the Pacific Ocean causing significant damage as far away as Hawaii, California, and Chile, thereby becoming the largest, most destructive tsunami in the Pacific Basin since 1960. Triggers on the seismic stations at Erimo, Hokkaido (ERM) and Matsushiro, Honshu (MAJO), alerted Pacific Tsunami Warning Center (PTWC) scientists 90 seconds after the earthquake began. Four minutes after its origin, and about one minute after the earthquake's rupture ended, PTWC issued an observatory message reporting a preliminary magnitude of 7.5. Eight minutes after origin time, the Japan Meteorological Agency (JMA) issued its first international tsunami message in its capacity as the Northwest Pacific Tsunami Advisory Center. In accordance with international tsunami warning system protocols, PTWC then followed with its first international tsunami warning message using JMA's earthquake parameters, including an Mw of 7.8. Additional Mwp, mantle wave, and W-phase magnitude estimations based on the analysis of later-arriving seismic data at PTWC revealed that the earthquake magnitude reached at least 8.8, and that a destructive tsunami would likely be crossing the Pacific Ocean. The earthquake damaged the nearest coastal sea-level station located 90 km from the epicenter in Ofunato, Japan. The NOAA DART sensor situated 600 km off the coast of Sendai, Japan, at a depth of 5.6 km recorded a tsunami wave amplitude of nearly two meters, making it by far the largest tsunami wave ever recorded by a DART sensor. Thirty minutes later, a coastal sea-level station at Hanasaki, Japan, 600 km from the epicenter, recorded a tsunami wave amplitude of

Drought forecasting and Warning provides the potential of reducing impacts to society due to drought events. The implementation of effective drought forecasting and warning, however, requires not only science to support reliable forecasting, but also adequate policy and societal response. Here we propose a protocol to develop drought forecasting and earlywarning based in the international cooperation of African and European institutions in the DEWFORA project (EC, 7th Framework Programme). The protocol includes four major phases that address the scientific knowledge and the social capacity to use the knowledge: (a) What is the science available? Evaluating how signs of impending drought can be detected and predicted, defining risk levels, and analysing of the signs of drought in an integrated vulnerability approach. (b) What are the societal capacities? In this the institutional framework that enables policy development is evaluated. The protocol gathers information on vulnerability and pending hazard in advance so that earlywarnings can be declared at sufficient lead time and drought mitigation planning can be implemented at an early stage. (c) How can science be translated into policy? Linking science indicators into the actions/interventions that society needs to implement, and evaluating how policy is implemented. Key limitations to planning for drought are the social capacities to implement earlywarning systems. Vulnerability assessment contributes to identify these limitations and therefore provides crucial information to policy development. Based on the assessment of vulnerability we suggest thresholds for management actions to respond to drought forecasts and link predictive indicators to relevant potential mitigation strategies. Vulnerability assessment is crucial to identify relief, coping and management responses that contribute to a more resilient society. (d) How can society benefit from the forecast? Evaluating how information is provided to

Morbidity and mortality associated with sepsis has gained widespread attention on a local, state, and national level, yet, it remains a complicated disorder that can be difficult to identify in a timely manner. Sepsis in obstetric patients further complicates the diagnosis as alterations in physiology related to pregnancy can mask sepsis indicators normally seen in the general population. If early signs of sepsis go unrecognized, septic shock can develop, leading to organ dysfunction and potential death. Maternal earlywarning tools have been designed to assist clinicians in recognizing early indications of illness. Through use of clinical pathway-specific tools, disease processes may be detected early, subsequently benefitting patients with aggressive treatment management and intervention.This article is the second in a series of three that discuss the importance of sepsis and septic shock in pregnancy. Risk factors, causes of sepsis, signs and symptoms, and maternal earlywarning tools are discussed.

Recently, the GPS measurements were used for retrieving the information on the various types of ionospheric responses to seismic events (earthquakes, seismic Rayleigh waves, and tsunami) which generate atmospheric waves propagating up to the ionospheric altitudes where the collisions between the neutrals and charge particles give rise to the motion of the ionospheric plasma. These experimental results can well be used in architecture of the future tsunami warning system. The point is an earlier (in comparison with seismological methods) detection of the ionospheric signal that can indicate the moment of tsunami generation. As an example we consider the two-dimensional distributions of the vertical total electron content (TEC) variations in the ionosphere both close to and far from the epicenter of the Japan undersea earthquake of March 11, 2011 using radio tomographic (RT) reconstruction of high-temporal-resolution (2-minute) data from the Japan and the US GPS networks. Near-zone TEC variations shows a diverging ionospheric perturbation with multi-component spectral composition emerging after the main shock. The initial phase of the disturbance can be used as an indicator of the tsunami generation and subsequently for the tsunami earlywarning. Far-zone TEC variations reveals distinct wave train associated with gravity waves generated by tsunami. According to observations tsunami arrives at Hawaii and further at the coast of Southern California with delay relative to the gravity waves. Therefore the gravity wave pattern can be used in the early tsunami warning. We support this scenario by the results of modeling with the parameters of the ocean surface perturbation corresponding to the considered earthquake. In addition it was observed in the modeling that at long distance from the source the gravity wave can pass ahead of the tsunami. The work was supported by the Russian Foundation for Basic Research (grants 11-05-01157 and 12-05-33065).

An earlywarning system is a data-based tool that helps predict which students are on the right path towards eventual graduation or other grade-appropriate goals. Through such systems, stakeholders at the school and district levels can view data from a wide range of perspectives and gain a deeper understanding of student data. This "Statewide…

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising earlywarning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide earlywarnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial earlywarning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial earlywarning signals on real spatial data. PMID:24658137

EarlyWarning Systems (EWSs) aggregate multiple sources of data to provide timely information to stakeholders about students in need of academic support. There is an increasing need to incorporate relevant data about student behaviors into the algorithms underlying EWSs to improve predictors of students' success or failure. Many EWSs currently…

Objectives: The current article describes the process evaluation of a social earlywarning system (SEWS) for the prevention of child maltreatment in the federal state of Hamburg. This prevention initiative targets expectant mothers and their partners including an initial screening of risk factors for child maltreatment, a subsequent structured…

This Solution-Finding Report provides information, requested by Tara Zuber with the Great Lakes Comprehensive Center (GLCC) at American Institutes for Research (AIR), for resources with evidence-based practices that look at the social and emotional causes that impact the lack of student learning and engagement, for GLCC's EarlyWarning Signs work.…

A Water Quality EarlyWarning System using On-line Toxicity Monitors (OTMs) has been deployed in the East Fork of the Little Miami River, Clermont County, OH. Living organisms have long been used to determine the toxicity of environmental samples. With advancements in electronic ...

Many buildings have been damaged due to earthquakes that occurred recently in Indonesia. The main cause of the damage is the large deformation of the building structural component cannot accommodate properly. Therefore, it is necessary to develop the Structural Health Monitoring System (SHMS) to measure precisely the deformation of the building structural component in the real time conditions. This paper presents the development of SHMS for reinforced concrete structural system. This monitoring system is based on deformation component such as strain of reinforcement bar, concrete strain, and displacement of reinforced concrete component. Since the deformation component has exceeded the limitmore » value, the warning message can be sent to the building occupies. This warning message has also can be performed as earlywarning system of the reinforced concrete structural system. The warning message can also be sent via Short Message Service (SMS) through the Global System for Mobile Communications (GSM) network. Hence, the SHMS should be integrated with internet modem to connect with GSM network. Additionally, the SHMS program is verified with experimental study of simply supported reinforced concrete beam. Verification results show that the SHMS has good agreement with experimental results.« less

The Virtual Seismologist (VS) earthquakeearlywarning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system that could potentially be implemented in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network since July 2008, and at the Northern California Seismic Network since February 2009. We discuss recent enhancements to the VS EEW algorithm that are being integrated into CISN ShakeAlert. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to initiate an event declaration, with the goal of reducing false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and the requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) into an on-site/regional approach capable of providing a continuously evolving stream of EEW information starting from the first P-detection. Real-time and offline analysis on Swiss and California waveform datasets indicate that the

Dynamical systems can undergo critical transitions where the system suddenly shifts from one stable state to another at a critical threshold called the tipping point. The decrease in recovery rate to equilibrium (critical slowing down) as the system approaches the tipping point can be used to identify the proximity to a critical transition. Several measures have been adopted to provide early indications of critical transitions that happen in a variety of complex systems. In this study, we use earlywarning indicators to predict subcritical Hopf bifurcation occurring in a thermoacoustic system by analyzing the observables from experiments and from a theoretical model. We find that the earlywarning measures perform as robust indicators in the presence and absence of external noise. Thus, we illustrate the applicability of these indicators in an engineering system depicting critical transitions. PMID:27767065

We performed a reconnaissance about EarlyWarning Systems (EWS) on Landslides (EWSL) in the countries of Central America. The advance of the EWSL began in the 1990-ies and accelerated dramatically after the regional disaster provoked by Hurricane Mitch in 1998. In the last decade, EarlyWarning Systems were intensely promoted by national and international development programs aimed on disaster prevention. EarlyWarning on landslides is more complicated than for other geological phenomena. But, we found information on more than 30 EWSL in the region. In practice, for example in planning, implementation and evaluation of development projects, it is often not clearly defined what exactly is an EarlyWarning System. Only few of the systems can be classified as true EWSL that means 1) being directly and solely aimed at persons living in the well-defined areas of greatest risk and 2) focusing their work on saving lives before the phenomenon impacts. There is little written information about the work of the EWSL after the initial phase. Even, there are no statistics whether they issued warnings, if the warnings were successful, how many people were evacuated, if there were few false alerts, etc.. Actually, we did not find a single report on a successful landslide warning issued by an EWSL. The lack of information is often due to the fact that communitarian EWSL are considered local structures and do not have a clearly defined position in the governmental hierarchy; there is little oversight and no qualified support and long-term support. The EWSL suffer from severe problems as lack of funding on the long term, low technical level, and insufficient support from central institutions. Often the EWSL are implemented by NGÓs with funding from international agencies, but leave the project alone after the initial phase. In many cases, the hope of the local people to get some protection against the landslide hazard is not really fulfilled. There is one case, where an EWSL with a

Bioindicators are biological processes, species or communities, which are used to assess changes in the environment or environmental quality. Theoretically, they could also be used to provide advanced warning of hazards. They are inexpensive, locally relevant, and can encourage stakeholder participation in earlywarning system development and maintenance. While bioindicators have been identified for environmental problems such as air pollution and water pollution, and have been used to assess health of ecosystems, little information is available on bioindicators for climate related hazards. This presentation reviews possible biodindicators for droughts, wildfires and tropical cyclones, based on the results of a literature review. It will also present results from a household survey of 36 communities in Kenya, Ghana and Burkina Faso. Indigenous knowledge offers a wealth of potential bioindicators; including animal and insect behavior, and plant phenology. Yet significant study is needed to verify these indicators and evaluate them against criteria such as specificity, variability, monotonicity, practicality and relevance. Bioindicators may not be specific to individual hazards and may provide limited advanced warning, as response often occurs after the actual onset of the hazard. Furthermore, indicators may become increasingly unreliable due to climate change itself. There is a need for a large-scale assessment of hazard bioindicators, which should also include forecasts of bioindicator change under global warming, and a cost-benefit analysis of the value of integrating bioindicators into earlywarning systems. Lessons can be drawn from ethnopharmacology. Coordinated research on this topic could contribute to the resilience of both ecosystems and human livelihoods.

The FEWS NET mission is to identify potentially food-insecure conditions early through the provision of timely and analytical hazard and vulnerability information. U.S. Government decision-makers act on this information to authorize mitigation and response activities. The U.S. Geological Survey (USGS) FEWS NET provides tools and data for monitoring and forecasting the incidence of drought and flooding to identify shocks to the food supply system that could lead to famine. Historically focused on Africa, the scope of the network has expanded to be global coverage. FEWS NET implementing partners include the USGS, National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), United States Agency for International Development (USAID), United States Department of Agriculture (USDA), and Chemonics International.

Since 1990, nearly one million people have died from the impacts of earthquakes. Reducing those impacts requires building a local seismic culture in which residents are aware of earthquake risks and value efforts to mitigate harm. Such efforts include earthquakeearlywarning (EEW) systems that provide seconds to minutes notice of pending shaking. Recent events in Mexico provide an opportunity to assess performance and perception of an EEW system and highlight areas for further improvement. We have learned that EEW systems, even imperfect ones, can help people prepare for earthquakes and build local seismic culture, both beneficial in reducing earthquake-related losses.

Earth's surface temperatures are projected to increase by ~1-4°C over the next century, threatening the future of global biodiversity and ecosystem stability. While this has fueled major progress in the field of physiological trait responses to warming, it is currently unclear whether routine population monitoring data can be used to predict temperature-induced population collapse. Here, we integrate trait performance theory with that of critical tipping points to test whether earlywarning signals can be reliably used to anticipate thermally induced extinction events. We find that a model parameterized by experimental growth rates exhibits critical slowing down in the vicinity of an experimentally tested critical threshold, suggesting that dynamical earlywarning signals may be useful in detecting the potentially precipitous onset of population collapse due to global climate change.

Electric field mills are used popularly for atmospheric electric field measurements. Atmospheric Electric Field variation is the primary signature for Lightning EarlyWarning systems. There is a characteristic change in the atmospheric electric field before lightning during a thundercloud formation.A voltage controlled variable capacitance is being proposed as a method for non-contacting measurement of electric fields. A varactor based mini electric field measurement system is developed, to detect any change in the atmospheric electric field and to issue lightning earlywarning system. Since this is a low-cost device, this can be used for developing countries which are facing adversities. A network of these devices can help in forming a spatial map of electric field variations over a region, and this can be used for more improved atmospheric electricity studies in developing countries.

Desert locust (Schistocerca gregaria, Forskål) plagues have historically had devastating consequences on food security in Africa and Asia. The current strategy to reduce the frequency of plagues and manage desert locust infestations is earlywarning and preventive control. To achieve this, the Food and Agriculture Organization of the United Nations operates one of the oldest, largest, and best-known migratory pest monitoring systems in the world. Within this system, remote sensing plays an important role in detecting rainfall and green vegetation. Despite recent technological advances in data management and analysis, communications, and remote sensing, monitoring desert locusts and preventing plagues in the years ahead will continue to be a challenge from a geopolitical and financial standpoint for affected countries and the international donor community. We present an overview of the use of remote sensing in desert locust earlywarning.

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Droughts severely impact Ethiopian agricultural production. Successful earlywarning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security earlywarning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought earlywarning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

Building early flood warning system is essential for the protection of the residents against flood hazards and make actions to mitigate the losses. This study implements AI technology for forecasting multi-step-ahead regional flood inundation maps during storm events. The methodology includes three major schemes: (1) configuring the self-organizing map (SOM) to categorize a large number of regional inundation maps into a meaningful topology; (2) building dynamic neural networks to forecast multi-step-ahead average inundated depths (AID); and (3) adjusting the weights of the selected neuron in the constructed SOM based on the forecasted AID to obtain real-time regional inundation maps. The proposed models are trained, and tested based on a large number of inundation data sets collected in regions with the most frequent and serious flooding in the river basin. The results appear that the SOM topological relationships between individual neurons and their neighbouring neurons are visible and clearly distinguishable, and the hybrid model can continuously provide multistep-ahead visible regional inundation maps with high resolution during storm events, which have relatively small RMSE values and high R2 as compared with numerical simulation data sets. The computing time is only few seconds, and thereby leads to real-time regional flood inundation forecasting and make early flood inundation warning system. We demonstrate that the proposed hybrid ANN-based model has a robust and reliable predictive ability and can be used for earlywarning to mitigate flood disasters.

To discuss the effects on earlywarning of measles, using the neural networks. Based on the available data through monthly and weekly reports on measles from January 1986 to August 2006 in Wuhan city. The modal was developed using the neural networks to predict and analyze the prevalence and incidence of measles. When the dynamic time series modal was established with back propagation (BP) networks consisting of two layers, if p was assigned as 9, the convergence speed was acceptable and the correlation coefficient was equal to 0.85. It was more acceptable for monthly forecasting the specific value, but better for weekly forecasting the classification under probabilistic neural networks (PNN). When data was big enough to serve the purpose, it seemed more feasible for earlywarning using the two-layer BP networks. However, when data was not enough, then PNN could be used for the purpose of prediction. This method seemed feasible to be used in the system for earlywarning.

The purpose of this study is to develop a real-time flood monitoring and earlywarning system in the northern portion of the province of Isabela, particularly the municipalities near Cagayan River. Ultrasonic sensing techniques have become mature and are widely used in the various fields of engineering and basic science. One of advantage of ultrasonic sensing is its outstanding capability to probe inside objective non-destructively because ultrasound can propagate through any kinds of media including solids, liquids and gases. This study focuses only on the water level detection and earlywarning system (via website and/or SMS) that alerts concern agencies and individuals for a potential flood event. Furthermore, inquiry system is also included in this study to become more interactive wherein individuals in the community could inquire the actual water level and status of the desired area or location affected by flood thru SMS keyword. The study aims in helping citizens to be prepared and knowledgeable whenever there is a flood. The novelty of this work falls under the utilization of the Arduino, ultrasonic sensors, GSM module, web-monitoring and SMS earlywarning system in helping stakeholders to mitigate casualties related to flood. The paper envisions helping flood-prone areas which are common in the Philippines particularly to the local communities in the province. Indeed, it is relevant and important as per needs for safety and welfare of the community.

The DEWS (Distant EarlyWarning System) [1] project, funded under the 6th Framework Programme of the European Union, has the objective to create a new generation of interoperable earlywarning systems based on an open sensor platform. This platform integrates OGC [2] SWE [3] compliant sensor systems for the rapid detection of hazardous events, like earthquakes, sea level anomalies, ocean floor occurrences, and ground displacements in the case of tsunami earlywarning. Based on the upstream information flow DEWS focuses on the improvement of downstream capacities of warning centres especially by improving information logistics for effective and targeted warning message aggregation for a multilingual environment. Multiple telecommunication channels will be used for the dissemination of warning messages. Wherever possible, existing standards have been integrated. The Command and Control User Interface (CCUI), a rich client application based on Eclipse RCP (Rich Client Platform) [4] and the open source GIS uDig [5], integrates various OGC services. Using WMS (Web Map Service) [6] and WFS (Web Feature Service) [7] spatial data are utilized to depict the situation picture and to integrate a simulation system via WPS (Web Processing Service) [8] to identify affected areas. Warning messages are compiled and transmitted in the OASIS [9] CAP (Common Alerting Protocol) [10] standard together with addressing information defined via EDXL-DE (Emergency Data Exchange Language - Distribution Element) [11]. Internal interfaces are realized with SOAP [12] web services. Based on results of GITEWS [13] - in particular the GITEWS Tsunami Service Bus [14] - the DEWS approach provides an implementation for tsunami earlywarning systems but other geological paradigms are going to follow, e.g. volcanic eruptions or landslides. Therefore in future also multi-hazard functionality is conceivable. The specific software architecture of DEWS makes it possible to dock varying sensors to the

Drought has long been recognized as falling into the category of incremental but long-term and cumulative environmental changes, also termed slow-onset or creeping events. These event types would include: air and water quality decline, desertification processes, deforestation and forest fragmentation, loss of biodiversity and habitats, and nitrogen overloading, among others. Climate scientists continue to struggle with recognizing the onset of drought and scientists and policy makers continue to debate the basis (i.e., criteria) for declaring an end to a drought. Risk-based management approaches to drought planning at the national and regional levels have been recommended repeatedly over the years but their prototyping, testing and operational implementation have been limited. This presentation will outline two avenues for disaster risk reduction in the context of drought (1) integrated earlywarning information systems, and (2) linking disaster risk reduction to climate change adaptation strategies. Adaptation involves not only using operational facilities and infrastructure to cope with the immediate problems but also leaving slack or reserve for coping with multiple stress problems that produce extreme impacts and surprise. Increasing the 'anticipatability' of an event, involves both monitoring of key indicators from appropriate baseline data, and observing earlywarning signs that assumptions in risk management plans are failing and critical transitions are occurring. Illustrative cases will be drawn from the IPCC Special Report on Managing the Risks of Extreme Events and Disasters (2011), the UN Global Assessment of Disaster Risk Reduction (2011) and implementation activities in which the author has been engaged. Most drought earlywarning systems have tended to focus on the development and use of physical system indicators and forecasts of trends and thresholds. We show that successful earlywarning systems that meet expectations of risk management also have

Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing earlywarning efforts based on NWP, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm EarlyWarning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the input dataset. We then optimise the configuration and show that also false alarms contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing earlywarning efforts based on numerical weather prediction, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm EarlyWarning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the type of input dataset. We then optimise the configuration and show that false alarms also contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

After the Chernobyl nuclear power plant accident in 1986, followed by the Fukushima Nuclear power plant accident 25 years later, it became obvious that real-time information is required to quickly gain radiological information. As a consequence, the European countries established earlywarning network systems with the aim to provide an immediate warning in case of a major radiological emergency, to supply reliable information on area dose rates, contamination levels, radioactivity concentrations in air and finally to assess public exposure. This is relevant for governmental decisions on intervention measures in an emergency situation. Since different methods are used by national environmental monitoring systems to measure area dose rate values and activity concentrations, there are significant differences in the results provided by different countries. Because European and neighboring countries report area dose rate data to a central data base operated on behalf of the European Commission, the comparability of the data is crucial for its meaningful interpretation, especially in the case of a nuclear accident with transboundary implications. Only by harmonizing measuring methods and data evaluation, is the comparability of the dose rate data ensured. This publication concentrates on technical requirements and methods with the goal to effectively harmonize area dose rate monitoring data provided by automatic earlywarning network systems. The requirements and procedures laid down in this publication are based on studies within the MetroERM project, taking into account realistic technical approaches and tested procedures.

The present contribution presents efforts towards the development of an operational EarlyWarning System for storm hazard prediction and mitigation. The system consists of a calibrated nested-model train which consists of specially calibrated Wave Watch III, SWAN and XBeach models. The numerical simulations provide daily forecasts of the hydrodynamic conditions, morphological change and overtopping risk at the area of interest. The model predictions are processed by a 'translation' module which is based on site-specific Storm Impact Indicators (SIIs) (Ciavola et al., 2011, Storm impacts along European coastlines. Part 2: lessons learned from the MICORE project, Environmental Science & Policy, Vol 14), and warnings are issued when pre-defined threshold values are exceeded. For the present site the selected SIIs were (i) the maximum wave run-up height during the simulations; and (ii) the dune-foot horizontal retreat at the end of the simulations. Both SIIs and pre-defined thresholds were carefully selected on the grounds of existing experience and field data. Four risk levels were considered, each associated with an intervention approach, recommended to the responsible coastal protection authority. Regular updating of the topography/bathymetry is critical for the performance of the storm impact forecasting, especially when there are significant morphological changes. The system can be extended to other critical problems, like implications of global warming and adaptive management strategies, while the approach presently followed, from model calibration to the earlywarning system for storm hazard mitigation, can be applied to other sites worldwide, with minor adaptations.

The tsunamigenic earthquake (M 8.8) that occurred offshore central Chile on 27 February 2010 can be classified as a typical subduction-zone earthquake. The effects of the ensuing tsunami have been devastating along the Chile coasts, and especially between the cities of Valparaiso and Talcahuano, and in the Juan Fernandez islands. The tsunami propagated across the entire Pacific Ocean, hitting with variable intensity almost all the coasts facing the basin. While the far-field propagation was quite well tracked almost in real-time by the warning centres and reasonably well reproduced by the forecast models, the toll of lives and the severity of the damage caused by the tsunami in the near-field occurred with no local alert nor warning and sadly confirms that the protection of the communities placed close to the tsunami sources is still an unresolved problem in the tsunami earlywarning field. The purpose of this study is two-fold. On one side we perform numerical simulations of the tsunami starting from different earthquake models which we built on the basis of the preliminary seismic parameters (location, magnitude and focal mechanism) made available by the seismological agencies immediately after the event, or retrieved from more detailed and refined studies published online in the following days and weeks. The comparison with the available records of both offshore DART buoys and coastal tide-gauges is used to put some preliminary constraints on the best-fitting fault model. The numerical simulations are performed by means of the finite-difference code UBO-TSUFD, developed and maintained by the Tsunami Research Team of the University of Bologna, Italy, which can solve both the linear and non-linear versions of the shallow-water equations on nested grids. The second purpose of this study is to use the conclusions drawn in the previous part in a tsunami earlywarning perspective. In the framework of the EU-funded project DEWS (Distant EarlyWarning System), we will

This EarlyWarning System (EWS) Implementation Guide is a supporting document for schools and districts that are implementing the National High School Center's EarlyWarning System (EWS) Tool v2.0. Developed by the National High School Center at the American Institutes for Research (AIR), the guide and tool support the establishment and…

Extreme scenarios of M 7.5+ earthquakes on the Red Mountain and Pitas Point faults can potentially generate significant local tsunamis in southern California. The maximum water elevation could be as large as 10 m in the nearshore region of Oxnard and Santa Barbara. Recent development in seismic array processing enables rapid tsunami prediction and earlywarning based on the back-projection approach (BP). The idea is to estimate the rupture size by back-tracing the seismic body waves recorded by stations at local and regional distances. A simplified source model of uniform slip is constructed and used as an input for tsunami simulations that predict the tsunami wave height and arrival time. We demonstrate the feasibility of this approach in southern California by implementing it in a simulated real-time environment and applying to a hypothetical M 7.7 Dip-slip earthquake scenario on the Pitas Point fault. Synthetic seismograms are produced using the SCEC broadband platform based on the 3D SoCal community velocity model. We use S-wave instead of P-wave to avoid S-minus-P travel times shorter than rupture duration. Two clusters of strong-motion stations near Bakersfield and Palmdale are selected to determine the back-azimuth of the strongest high-frequency radiations (0.5-1 Hz). The back-azimuths of the two clusters are then intersected to locate the source positions. The rupture area is then approximated by enclosing these BP radiators with an ellipse or a polygon. Our preliminary results show that the extent of 1294 square kilometers rupture area and magnitude of 7.6 obtained by this approach is reasonably close to the 1849 square kilometers and 7.7 of the input model. The average slip of 7.3 m is then estimated according to the scaling relation between slip and rupture area, which is close to the actual average dislocation amount, 8.3 m. Finally, a tsunami simulation is conducted to assess the wave height and arrival time. The errors of -3 to +9 s in arrival time

In times of cloud computing and ubiquitous computing the use of concepts and paradigms introduced by information and communications technology (ICT) have to be considered even for earlywarning systems (EWS). Based on the experiences and the knowledge gained in research projects new technologies are exploited to implement a cloud-based and web-based platform - the TRIDEC Cloud - to open up new prospects for EWS. The platform in its current version addresses tsunami earlywarning and mitigation. It merges several complementary external and in-house cloud-based services for instant tsunami propagation calculations and automated background computation with graphics processing units (GPU), for web-mapping of hazard specific geospatial data, and for serving relevant functionality to handle, share, and communicate threat specific information in a collaborative and distributed environment. The TRIDEC Cloud can be accessed in two different modes, the monitoring mode and the exercise-and-training mode. The monitoring mode provides important functionality required to act in a real event. So far, the monitoring mode integrates historic and real-time sea level data and latest earthquake information. The integration of sources is supported by a simple and secure interface. The exercise and training mode enables training and exercises with virtual scenarios. This mode disconnects real world systems and connects with a virtual environment that receives virtual earthquake information and virtual sea level data re-played by a scenario player. Thus operators and other stakeholders are able to train skills and prepare for real events and large exercises. The GFZ German Research Centre for Geosciences (GFZ), the Kandilli Observatory and Earthquake Research Institute (KOERI), and the Portuguese Institute for the Sea and Atmosphere (IPMA) have used the opportunity provided by NEAMWave14 to test the TRIDEC Cloud as a collaborative activity based on previous partnership and commitments at

In Norway, shallow slides and debris flows occur as a combination of high-intensity precipitation, snowmelt, high groundwater level and saturated soil. Many events have occurred in the last decades and are often associated with (or related to) floods events, especially in the Southern of Norway, causing significant damages to roads, railway lines, buildings, and other infrastructures (i.e November 2000; August 2003; September 2005; November 2005; Mai 2008; June and Desember 2011). Since 1989 the Norwegian Water Resources and Energy Directorate (NVE) has had an operational 24 hour flood forecasting system for the entire country. From 2009 NVE is also responsible to assist regions and municipalities in the prevention of disasters posed by landslides and snow avalanches. Besides assisting the municipalities through implementation of digital landslides inventories, susceptibility and hazard mapping, areal planning, preparation of guidelines, realization of mitigation measures and helping during emergencies, NVE is developing a regional scale debris flow warning system that use hydrological models that are already available in the flood warning systems. It is well known that the application of rainfall thresholds is not sufficient to evaluate the hazard for debris flows and shallow slides, and soil moisture conditions play a crucial role in the triggering conditions. The information on simulated soil and groundwater conditions and water supply (rain and snowmelt) based on weather forecast, have proved to be useful variables that indicate the potential occurrence of debris flows and shallow slides. Forecasts of runoff and freezing-thawing are also valuable information. The earlywarning system is using real-time measurements (Discharge; Groundwater level; Soil water content and soil temperature; Snow water equivalent; Meteorological data) and model simulations (a spatially distributed version of the HBV-model and an adapted version of 1-D soil water and energy balance

In Malaysia, rain induced landslides are occurring more often than before. The Malaysian Government allocates millions of Malaysian Ringgit for slope monitoring and slope failure remedial measures in the budget every year. In rural areas, local authorities also play a major role in monitoring the slope to prevent casualty by giving information to the residents who are staying near to the slopes. However, there are thousands of slopes which are classified as high risk slopes in Malaysia. Implementing site monitoring system in these slopes to monitor the movement of the soil in the slopes, predicting the occurrence of slopes failure and establishing earlywarning system are too costly and almost impossible. In our study, we propose Accumulated Rainfall vs. Rainfall Intensity prediction method to predict the slope failure by referring to the predicted rainfall data from radar and the rain volume from rain gauges. The critical line which determines if the slope is in danger, is generated by simulator with well-surveyed the soil property in the slope and compared with historical data. By establishing such predicting system, the slope failure warning information can be obtained and disseminated to the surroundings via SMS, internet and siren. However, establishing the earlywarning dissemination system is not enough in disaster prevention, educating school children and the community by giving knowledge on landslides, such as landslide's definition, how and why does the slope failure happen and when will it fail, to raise the risk awareness on landslides will reduce landslides casualty, especially in rural area. Moreover, showing video on the risk and symptom of landslides in school will also help the school children gaining the knowledge of landslides. Generating hazard map and landslides historical data provides further information on the occurrence of the slope failure. In future, further study on fine tuning of landslides prediction method, applying IT technology to

An essential part of earlywarning systems and systems for crisis management are decision support systems that facilitate communication and collaboration. Often official policies specify how different organizations collaborate and what information is communicated to whom. For earlywarning systems it is crucial that information is exchanged dynamically in a timely manner and all participants get exactly the information they need to fulfil their role in the crisis management process. Information technology obviously lends itself to automate parts of the process. We have experienced however that in current operational systems the information logistics processes are hard-coded, even though they are subject to change. In addition, systems are tailored to the policies and requirements of a certain organization and changes can require major software refactoring. We seek to develop a system that can be deployed and adapted to multiple organizations with different dynamic runtime policies. A major requirement for such a system is that changes can be applied locally without affecting larger parts of the system. In addition to the flexibility regarding changes in policies and processes, the system needs to be able to evolve; when new information sources become available, it should be possible to integrate and use these in the decision process. In general, this kind of flexibility comes with a significant increase in complexity. This implies that only IT professionals can maintain a system that can be reconfigured and adapted; end-users are unable to utilise the provided flexibility. In the business world similar problems arise and previous work suggested using business process management systems (BPMS) or workflow management systems (WfMS) to guide and automate earlywarning processes or crisis management plans. However, the usability and flexibility of current WfMS are limited, because current notations and user interfaces are still not suitable for end-users, and workflows

While there are well established earlywarning systems for a number of natural phenomena (e.g. earthquakes, catastrophic fires, tsunamis), we do not have an earlywarning system for biodiversity. Yet, we are losing species at an unprecedented rate, and this especially occurs in tropical rainforests, the biologically richest but most eroded biome on earth. Unfortunately, there is a chronic gap in standardized and pan-tropical data in tropical forests, affecting our capacity to monitor changes and anticipate future scenarios. The Tropical Ecology, Assessment and Monitoring (TEAM) Network was established to contribute addressing this issue, as it generates real time data to monitor long-term trends in tropical biodiversity and guide conservation practice. We present the Network and focus primarily on the Terrestrial Vertebrates protocol, that uses systematic camera trapping to detect forest mammals and birds, and secondarily on the Zone of Interaction protocol, that measures changes in the anthroposphere around the core monitoring area. With over 3 million images so far recorded, and managed using advanced information technology, TEAM has created the most important data set on tropical forest mammals globally. We provide examples of site-specific and global analyses that, combined with data on anthropogenic disturbance collected in the larger ecosystem where monitoring sites are, allowed us to understand the drivers of changes of target species and communities in space and time. We discuss the potential of this system as a candidate model towards setting up an earlywarning system that can effectively anticipate changes in coupled human-natural system, trigger management actions, and hence decrease the gap between research and management responses. In turn, TEAM produces robust biodiversity indicators that meet the requirements set by global policies such as the Aichi Biodiversity Targets. Standardization in data collection and public sharing of data in near real time

Introduction Nowadays GNSS technologies are used for a large variety of precise positioning applications. The accuracy can reach the mm level depending on the data analysis methods. GNSS technologies thus offer a high potential to support tsunami earlywarning systems, e.g., by detection of ground motions due to earthquakes and of tsunami waves on the ocean by GNSS instruments on a buoy. Although GNSS-based precise positioning is a standard method, it is not yet common to apply this technique under tight time constraints and, hence, in the absence of precise satellite orbits and clocks. The new developed GNSS-based component utilises on- and offshore measured GNSS data and is the first system of its kind that was integrated into an operational earlywarning system. (Indonesian Tsunami EarlyWarning Centre INATEWS, inaugurated at BMKG, Jakarta on November, 11th 2008) Motivation After the Tsunami event of 26th December 2004 the German government initiated the GITEWS project (German Indonesian Tsunami EarlyWarning System) to develop a tsunami earlywarning system for Indonesia. The GFZ Potsdam (German Research Centre for Geosciences) as the consortial leader of GITEWS also covers several work packages, most of them related to sensor systems. The geodetic branch (Department 1) of the GFZ was assigned to develop a GNSS-based component. Brief system description The system covers all aspects from sensor stations with new developed hard- and software designs, manufacturing and installation of stations, real-time data transfer issues, a new developed automatic near real-time data processing and a graphical user interface for earlywarning centre operators including training on the system. GNSS sensors are installed on buoys, at tide gauges and as real-time reference stations (RTR stations), either stand-alone or co-located with seismic sensors. The GNSS data are transmitted to the warning centre where they are processed in a near real-time data processing chain. For

Ionospheric plasma disturbances after a large tsunami can be detected by measurement of the total electron content (TEC) between a Global Positioning System (GPS) satellite and its ground-based receivers. TEC depression lasting for a few minutes to tens of minutes termed as tsunami ionospheric hole (TIH) is formed above the tsunami source area. Here we describe the quantitative relationship between initial tsunami height and the TEC depression rate caused by a TIH from seven tsunamigenic earthquakes in Japan and Chile. We found that the percentage of TEC depression and initial tsunami height are correlated and the largest TEC depressions appear 10 to 20 minutes after the main shocks. Our findings imply that Ionospheric TEC measurement using the existing ground receiver networks could be used in an earlywarning system for near-field tsunamis that take more than 20 minutes to arrive in coastal areas.

Ionospheric plasma disturbances after a large tsunami can be detected by measurement of the total electron content (TEC) between a Global Positioning System (GPS) satellite and its ground-based receivers. TEC depression lasting for a few minutes to tens of minutes termed as tsunami ionospheric hole (TIH) is formed above the tsunami source area. Here we describe the quantitative relationship between initial tsunami height and the TEC depression rate caused by a TIH from seven tsunamigenic earthquakes in Japan and Chile. We found that the percentage of TEC depression and initial tsunami height are correlated and the largest TEC depressions appear 10 to 20 minutes after the main shocks. Our findings imply that Ionospheric TEC measurement using the existing ground receiver networks could be used in an earlywarning system for near-field tsunamis that take more than 20 minutes to arrive in coastal areas. PMID:27905487

Recently, tsunami earlywarning technique has been improved using tsunami waveforms observed at the ocean bottom pressure gauges such as NOAA DART system or DONET and S-NET systems in Japan. However, for tsunami earlywarning of near field tsunamis, it is essential to determine appropriate source models using seismological analysis before large tsunamis hit the coast, especially for tsunami earthquakes which generated significantly large tsunamis. In this paper, we develop a technique to determine appropriate source models from which appropriate tsunami inundation along the coast can be numerically computed The technique is tested for four large earthquakes, the 1992 Nicaragua tsunami earthquake (Mw7.7), the 2001 El Salvador earthquake (Mw7.7), the 2004 El Astillero earthquake (Mw7.0), and the 2012 El Salvador-Nicaragua earthquake (Mw7.3), which occurred off Central America. In this study, fault parameters were estimated from the W-phase inversion, then the fault length and width were determined from scaling relationships. At first, the slip amount was calculated from the seismic moment with a constant rigidity of 3.5 x 10**10N/m2. The tsunami numerical simulation was carried out and compared with the observed tsunami. For the 1992 Nicaragua tsunami earthquake, the computed tsunami was much smaller than the observed one. For the 2004 El Astillero earthquake, the computed tsunami was overestimated. In order to solve this problem, we constructed a depth dependent rigidity curve, similar to suggested by Bilek and Lay (1999). The curve with a central depth estimated by the W-phase inversion was used to calculate the slip amount of the fault model. Using those new slip amounts, tsunami numerical simulation was carried out again. Then, the observed tsunami heights, run-up heights, and inundation areas for the 1992 Nicaragua tsunami earthquake were well explained by the computed one. The other tsunamis from the other three earthquakes were also reasonably well explained

The town of Machu Picchu, Peru, is linked to Ollantaytambo and Cusco by rail and serves as the main station for the 400,000+ tourists visiting Machu Picchu. Due to the tourist industry the town grown threefold in population in the past two decades. Today, due to the limited availability of low-lying ground, construction is occurring higher up on the unstable valley slopes. The town is located at 2000 m asl while the surrounding peaks rise to over 4000 m asl. Slopes range from < 10° on the valley floor to > 70° in the surrounding granite mountains. The town has grown on the downstream right bank of the Vilcanota River, at the confluence of the Alcamayo and the Aguas Calientes Rivers. Broadly, a dry winter season runs from May to August with a rainy summer season running from October to March. The rainy months provide around 80% of the annual rainfall average, which ranges from 1,600 to 2,300 mm. Seasonal temperature variations are considered modest. An assessment of the geohazards in and around the town has been undertaken. Those of particular concern to the town are 1) large rocks falling onto the town and/or the rail line, 2) flash flooding by any one of its three rivers, and 3) mudflows and landslides. To improve the existing municipal warning system a prototype earlywarning system incorporating suitable technologies that could monitor weather, river flow and slope satability was installed along the Aguas Calientes River in 2009. This has a distributed modular construction allowing most components to be installed, maintained, swapped, salvaged, repaired and/or replaced by local technicians. A diverse set of candidate power, communication and sensor technologies was deployed and evaluated. Most of the candidate technologies had never been deployed in similar terrain, altitude or weather. The successful deployment of the prototype proved that it is technically feasible to develop earlywarning capacity in the town.

This study proposes a prototype of the regional early flood inundation warning system in Tainan City, Taiwan. The AI technology is used to forecast multi-step-ahead regional flood inundation maps during storm events. The computing time is only few seconds that leads to real-time regional flood inundation forecasting. A database is built to organize data and information for building real-time forecasting models, maintaining the relations of forecasted points, and displaying forecasted results, while real-time data acquisition is another key task where the model requires immediately accessing rain gauge information to provide forecast services. All programs related database are constructed in Microsoft SQL Server by using Visual C# to extracting real-time hydrological data, managing data, storing the forecasted data and providing the information to the visual map-based display. The regional early flood inundation warning system use the up-to-date Web technologies driven by the database and real-time data acquisition to display the on-line forecasting flood inundation depths in the study area. The friendly interface includes on-line sequentially showing inundation area by Google Map, maximum inundation depth and its location, and providing KMZ file download of the results which can be watched on Google Earth. The developed system can provide all the relevant information and on-line forecast results that helps city authorities to make decisions during typhoon events and make actions to mitigate the losses.

In the wake of healthcare disasters, such as the appalling failures of care uncovered in Mid Staffordshire, inquiries and investigations often point to a litany of earlywarnings and weak signals that were missed, misunderstood or discounted by the professionals and organisations charged with monitoring the safety and quality of care. Some of the most urgent challenges facing those responsible for improving and regulating patient safety are therefore how to identify, interpret, integrate and act on the earlywarnings and weak signals of emerging risks-before those risks contribute to a disastrous failure of care. These challenges are fundamentally organisational and cultural: they relate to what information is routinely noticed, communicated and attended to within and between healthcare organisations-and, most critically, what is assumed and ignored. Analysing these organisational and cultural challenges suggests three practical ways that healthcare organisations and their regulators can improve safety and address emerging risks. First, engage in practices that actively produce and amplify fleeting signs of ignorance. Second, work to continually define and update a set of specific fears of failure. And third, routinely uncover and publicly circulate knowledge on the sources of systemic risks to patient safety and the improvements required to address them. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Nipple discharge (ND) can be the earliest presenting symptom of breast cancer. We hereby present two cases of breast cancer with no palpable mass manifesting as isolated ND, which was whitish in color. In both cases, cytology of the discharge revealed highly pleomorphic cells indicating a high grade malignancy. Mammography showed diffuse, extensive microcalcifications. Simple mastectomy with axillary clearance was done. Histology in both cases revealed diffusely spreading intraductal carcinoma, with focus of microinvasion in one case. ND if scanty or not blood stained is often ignored by the patients and at times, the clinicians. This article highlights that ND can be an earlywarning sign of intraductal carcinomas that are non-invasive in early stage. Irrespective of the color or nature of the discharge, unilateral ND needs to be evaluated. Proper clinical assessment, cytological evaluation of the ND, and mammography ought to be performed in all such cases. Considering the low level of awareness in women regarding the warning signs of breast cancer, the current focus is to create “breast awareness.” Women should be sensitized to recognize any unusual changes in their breasts and report to their health care providers at the earliest. PMID:23189234

Providing an earlywarning of supernova burst neutrinos is of importance in studying both supernova dynamics and neutrino physics. The Daya Bay Reactor Neutrino Experiment, with a unique feature of multiple liquid scintillator detectors, is sensitive to the full energy spectrum of supernova burst electron-antineutrinos. By utilizing 8 Antineutrino Detectors (ADs) in the three different experimental halls which are about 1 km's apart from each other, we obtain a powerful and prompt rejection of muon spallation background than single-detector experiments with the same target volume. A dedicated trigger system embedded in the data acquisition system has been installed to allow the detection of a coincidence of neutrino signals of all ADs via an inverse beta-decay (IBD) within a 10-second window, thus providing a robust earlywarning of a supernova occurrence within the Milky Way. An 8-AD associated supernova trigger table has been established theoretically to tabulate the 8-AD event counts' coincidence vs. the trigger rate. As a result, a golden trigger threshold, i.e. with a false alarm rate < 1/3-months, can be set as low as 6 candidates among the 8 detectors, leading to a 100% detection probability for all 1987A type supernova bursts at the distance to the Milky Way center and a 96% detection probability to those at the edge of the Milky Way.

In the military field, the performance evaluation of early-warning aircraft deployment or construction is always an important problem needing to be explored. As an effective approach of enterprise management and performance evaluation, Balanced Score Card (BSC) attracts more and more attentions and is studied more and more widely all over the world. It can also bring feasible ideas and technical approaches for studying the issue of the performance evaluation of the deployment or construction of early-warning aircraft which is the important component in early-warning detection system of systems (SoS). Therefore, the deep explored researches are carried out based on the previously research works. On the basis of the characteristics of space exploration and aerial detection effectiveness of early-warning detection SoS and the cardinal principle of BSC are analyzed simply, and the performance evaluation framework of the deployment or construction of early-warning aircraft is given, under this framework, aimed at the evaluation issue of aerial detection effectiveness of early-warning detection SoS with the cooperation efficiency factors of the early-warning aircraft and other land based radars, the evaluation indexes are further designed and the relative evaluation model is further established, especially the evaluation radar chart being also drawn to obtain the evaluation results from a direct sight angle. Finally, some practical computer simulations are launched to prove the validity and feasibility of the research thinking and technologic approaches which are proposed in the paper.

A schematic of the components of regional earlywarning systems for rainfall-induced landslides is herein proposed, based on a clear distinction between warning models and warning systems. According to this framework an earlywarning system comprises a warning model as well as a monitoring and warning strategy, a communication strategy and an emergency plan. The paper proposes the evaluation of regional landslide warning models by means of an original approach, called the "event, duration matrix, performance" (EDuMaP) method, comprising three successive steps: identification and analysis of the events, i.e., landslide events and warning events derived from available landslides and warnings databases; definition and computation of a duration matrix, whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the earlywarning model performance by means of performance criteria and indicators applied to the duration matrix. During the first step the analyst identifies and classifies the landslide and warning events, according to their spatial and temporal characteristics, by means of a number of model parameters. In the second step, the analyst computes a time-based duration matrix with a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warning data from the municipal earlywarning system operating in Rio de Janeiro (Brazil).

On 27th and 28th November 2012 the first European-wide tsunami exercise took place under the auspices of UNESCO Intergovernmental Coordination Group for the Tsunami EarlyWarning and Mitigation System in the North-eastern Atlantic, the Mediterranean and connected seas (ICG/NEAMTWS). Four international scenarios were performed - one for each candidate tsunami watch provider France, Greece, Portugal and Turkey. Their task was to generate and disseminate tsunami warning bulletins in-time and in compliance with the official NEAMTWS specifications. The Instituto Português do Mar e da Atmosfera (IPMA, [1]) in Lissabon and the Kandilli Observatory and Earthquake Research Institute (KOERI [2]) in Istanbul are the national agencies of Portugal and Turkey responsible for tsunami earlywarning. Both institutes are partners in the TRIDEC [3] project and were using the TRIDEC Natural Crisis Management (NCM) system during NEAMWave exercise. The software demonstrated the seamless integration of diverse components including sensor systems, simulation data, and dissemination hardware. The functionalities that were showcased significantly exceeded the internationally agreed range of capabilities. Special attention was given to the Command and Control User Interface (CCUI) serving as central application for the operator. Its origins lie in the DEWS project [4] but numerous new functionalities were added to master all requirements defined by the complex NEAMTWS workflows. It was of utmost importance to develop an application handling the complexity of tsunami science but providing a clearly arranged and comprehensible interface that disburdens the operator during time-critical hazard situations. [1] IPMA: www.ipma.pt/ [2] KOERI: www.koeri.boun.edu.tr/ [3] TRIDEC: www.tridec-online.eu [4] DEWS: www.dews-online.org

Over the past decade, the number of open-ocean gauges capable of parsing information about a passing tsunami has steadily increased, particularly through national cable networks and international buoyed efforts such as the Deep-ocean Assessment and Reporting of Tsunami (DART). This information is analyzed to disseminate tsunami warnings to affected regions. However, most current warnings that incorporate tsunami are directed at mid- and far-field localities. In this study, we analyze the region surrounding four seismically active subduction zones, Cascadia, Japan, Chile, and Java, for their potential to facilitate local tsunami earlywarning using such systems. We assess which locations currently have instrumentation in the right locations for direct tsunami observations with enough time to provide useful warning to the nearest affected coastline—and which are poorly suited for such systems. Our primary findings are that while some regions are ill-suited for this type of earlywarning, such as the coastlines of Chile, other localities, like Java, Indonesia, could incorporate direct tsunami observations into their hazard forecasts with enough lead time to be effective for coastal community emergency response. We take into account the effect of tsunami propagation with regard to shallow bathymetry on the fore-arc as well as the effect of earthquake source placement. While it is impossible to account for every type of off-shore tsunamigenic event in these locales, this study aims to characterize a typical large tsunamigenic event occurring in the shallow part of the megathrust as a guide in what is feasible with early tsunami warning.

AGU 2015 Fall Meeting Session ID#: 7598 Remote Sensing Applications for Water Resources Management Land Surface Modeling Applications for Famine EarlyWarning James Verdin, USGS EROS Christa Peters-Lidard, NASA GSFC Amy McNally, NASA GSFC, UMD/ESSIC Kristi Arsenault, NASA GSFC, SAIC Shugong Wang, NASA GSFC, SAIC Sujay Kumar, NASA GSFC, SAIC Shrad Shukla, UCSB Chris Funk, USGS EROS Greg Fall, NOAA Logan Karsten, NOAA, UCAR Famine earlywarning has traditionally required close monitoring of agro-climatological conditions, putting them in historical context, and projecting them forward to anticipate end-of-season outcomes. In recent years, it has become necessary to factor in the effects of a changing climate as well. There has also been a growing appreciation of the linkage between food security and water availability. In 2009, Famine EarlyWarning Systems Network (FEWS NET) science partners began developing land surface modeling (LSM) applications to address these needs. With support from the NASA Applied Sciences Program, an instance of the Land Information System (LIS) was developed to specifically support FEWS NET. A simple crop water balance model (GeoWRSI) traditionally used by FEWS NET took its place alongside the Noah land surface model and the latest version of the Variable Infiltration Capacity (VIC) model, and LIS data readers were developed for FEWS NET precipitation forcings (NOAA's RFE and USGS/UCSB's CHIRPS). The resulting system was successfully used to monitor and project soil moisture conditions in the Horn of Africa, foretelling poor crop outcomes in the OND 2013 and MAM 2014 seasons. In parallel, NOAA created another instance of LIS to monitor snow water resources in Afghanistan, which are an early indicator of water availability for irrigation and crop production. These successes have been followed by investment in LSM implementations to track and project water availability in Sub-Saharan Africa and Yemen, work that is now underway. Adoption of

The 2017 Mw 8.1, Tehuantepec earthquake generated a moderated tsunami, which was registered in near-field tide gauges network activating a tsunami threat state for Mexico issued by PTWC. In the case of Chile, the forecast of tsunami waves indicate amplitudes less than 0.3 meters above the tide level, advising an informative state of threat, without activation of evacuation procedures. Nevertheless, during sea level monitoring of network we detect wave amplitudes (> 0.3 m) indicating a possible change of threat state. Finally, NTWS maintains informative level of threat based on mathematical filtering analysis of sea level records. After 2010 Mw 8.8, Maule earthquake, the Chilean National Tsunami Warning System (NTWS) has increased its observational capabilities to improve early response. Most important operational efforts have focused on strengthening tide gauge network for national area of responsibility. Furthermore, technological initiatives as Integrated Tsunami Prediction and Warning System (SIPAT) has segmented the area of responsibility in blocks to focus earlywarning and evacuation procedures on most affected coastal areas, while maintaining an informative state for distant areas of near-field earthquake. In the case of far-field events, NTWS follow the recommendations proposed by Pacific Tsunami Warning Center (PTWC), including a comprehensive monitoring of sea level records, such as tide gauges and DART (Deep-Ocean Assessment and Reporting of Tsunami) buoys, to evaluate the state of tsunami threat in the area of responsibility. The main objective of this work is to analyze the first-order physical processes involved in the far-field propagation and coastal impact of tsunami, including implications for decision-making of NTWS. To explore our main question, we construct a finite-fault model of the 2017, Mw 8.1 Tehuantepec earthquake. We employ the rupture model to simulate a transoceanic tsunami modeled by Neowave2D. We generate synthetic time series at

Monitoring and control of water treatment plants play an essential role in ensuring high quality drinking water and avoiding health-related problems or economic losses. The most common quality variables, which can be used also for assessing the efficiency of the water treatment process, are turbidity and residual levels of coagulation and disinfection chemicals. In the present study, the trend indices are developed from scaled measurements to detect warning signs of changes in the quality variables of drinking water and some operating condition variables that strongly affect water quality. The scaling is based on monotonically increasing nonlinear functions, which are generated with generalized norms and moments. Triangular episodes are classified with the trend index and its derivative. Deviation indices are used to assess the severity of situations. The study shows the potential of the described trend analysis as a predictive monitoring tool, as it provides an advantage over the traditional manual inspection of variables by detecting changes in water quality and giving earlywarnings.

The Common Alerting Protocol (CAP) [1] is an XML-based data format for exchanging public warnings and emergencies between alerting technologies. In China, from local communities to entire nations, there was a patchwork of specialized hazard public alerting systems. And each system was often designed just for certain emergency situations and for certain communications media. Application took place in the NEWAS (National EarlyWarning Alerting Systems) [2]project where CAP serves as central message to integrate all kind of hazard situations, including the natural calamity, accident disaster, public health emergency , social safety etc. Officially operated on May 2015, NEWAS now has completed docking work with 14 departments including civil administration, safety supervision, forestry, land, water conservancy, earthquake, traffic, meteorology, agriculture, tourism, food and drug supervision, public security and oceanic administration. Thus, several items in CAP has been modified, redefined and extended according to the various grading standards and publishing strategies, as well as the characteristics of Chinese Geocoding. NEWAS successfully delivers information to end users through 4 levels (i.e. State, province, prefecture and county) structure and by various means. [1] CAP, http://www.oasis-emergency.org/cap [2] http://www.12379.cn/

EarlyWarning Systems (EWS) for drought are often based on risk models that do not, or marginally, take into account the vulnerability factor. The multifaceted nature of drought (hydrological, meteorological, and agricultural) is source of coexistence for different ways to measure this phenomenon and its effects. The latter, together with the complexity of impacts generated by this hazard, causes the current underdevelopment of drought EWS compared to other hazards. In Least Developed Countries, where drought events causes the highest numbers of affected people, the importance of correct monitoring and forecasting is considered essential. Existing earlywarning and monitoring systems for drought produced at different geographic levels, provide only in a few cases an actual spatial model that tries to describe the cause-effect link between where the hazard is detected and where impacts occur. Integrate vulnerability information in such systems would permit to better estimate affected zones and livelihoods, improving the effectiveness of produced hazard-related datasets and maps. In fact, the need of simplification and, in general, of a direct applicability of scientific outputs is still a matter of concern for field experts and earlywarning products end-users. Even if the surplus of hazard related information produced right after catastrophic events has, in some cases, led to the creation of specific data-sharing platforms, the conveyed meaning and usefulness of each product has not yet been addressed. The present work is an attempt to fill this gap which is still an open issue for the scientific community as well as for the humanitarian aid world. The study aims at conceiving a simplified vulnerability model to embed into an existing EWS for drought, which is based on the monitoring of vegetation phenological parameters and the Standardized Precipitation Index, both produced using free satellite derived datasets. The proposed vulnerability model includes (i) a

Warning systems commonly use information provided by networks of sensors able to monitor and detect impending disasters, aggregate and condense these information to provide reliable information to a decision maker whether to warn or not, disseminates the warning message and provide this information to people at risk. Ultimate aim is to enable those in danger to make decisions (e.g. initiate protective actions for buildings) and to take action to safe their lives. This involves very complex issues when considering all four elements of earlywarning systems (UNISDR-PPEW), namely (1) risk knowledge, (2) monitoring and warning service, (3) dissemination and communication, (4) response capability with the ultimate aim to gain as much time as possible to empower individuals and communities to act in an appropriate manner to reduce injury, loss of life, damage to property and the environment and loss of livelihoods. Commonly most warning systems feature strengths and main attention on the technical/structural dimension (monitoring & warning service, dissemination tools) with weaknesses and less attention on social/cultural dimension (e.g. human response capabilities, defined warning chain to and knowing what to do by the people). Also, the use of risk knowledge in earlywarning most often is treated in a theoretical manner (knowing that it is somehow important), yet less in an operational, practical sense. Risk assessments and risk maps help to motivate people, prioritise earlywarning system needs and guide preparations for response and disaster prevention activities. Beyond this risk knowledge can be seen as a tie between national level earlywarning and community level reaction schemes. This presentation focuses on results, key findings and lessons-learnt related to tsunami risk assessment in the context of earlywarning within the GITEWS (German-Indonesian Tsunami EarlyWarning) project. Here a novel methodology reflecting risk information needs in the earlywarning

The financial crisis clearly illustrated the importance of characterizing the level of `systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998-2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear - but unpredictable - signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an earlywarning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

Earlywarning systems (EWS) are becoming effective tools for real time mitigation of the harmful effects arising from widely different hazards, which range from famine to financial crisis, malicious attacks, industrial accidents, natural catastrophes, etc. Earlywarning of natural catastrophic events allows to implement both alert systems and real time prevention actions for the safety of people and goods exposed to the risk However the effective implementation of earlywarning methods is hindered by the lack of a specific juridical frame. Under a juridical point of view, in fact, EWS and in general all the activities of prevention need a careful regulation, mainly with regards to responsibility and possible compensation for damage caused by the implemented actions. A preventive alarm, in fact, has an active influence on infrastructures in control of public services which in turn will suffer suspensions or interruptions because of the earlywarning actions. From here it is necessary to possess accurate normative references related to the typology of structures or infrastructures upon which the activity of readiness acts; the progressive order of suspension of public services; the duration of these suspensions; the corporate bodies or administrations that are competent to assume such decisions; the actors responsible for the consequences of false alarm, missed or delayed alarms; the mechanisms of compensation for damage; the insurance systems; etc In the European Union EWS are often quoted as preventive methods of mitigation of the risk. Nevertheless, a juridical notion of EWS of general use is not available. In fact, EW is a concept that finds application in many different circles, each of which require specific adaptations, and may concern subjects for which the European Union doesn't have exclusive competence as may be the responsibility of the member states to assign the necessary regulations. In so far as the juridical arrangement of the EWS, this must be

The identification of earlywarning signals for regime shifts in ecosystems is of crucial importance given their impact in terms of economic and social effects. We present here the results of a theoretical study on the desertification transition in semiarid ecosystems under external stress. We performed numerical simulations based on a stochastic cellular automaton model, and we studied the dynamics of the vegetation clusters in terms of percolation theory, assumed as an effective tool for analyzing the geometrical properties of the clusters. Focusing on the role played by the strength of external stresses, measured by the mortality rate m, we followed the progressive degradation of the ecosystem for increasing m, identifying different stages: first, the fragmentation transition occurring at relatively low values of m, then the desertification transition at higher mortality rates, and finally the full desertification transition corresponding to the extinction of the vegetation and the almost complete degradation of the soil, attained at the maximum value of m. For each transition we calculated the spanning probabilities as functions of m and the percolation thresholds according to different spanning criteria. The identification of the different thresholds is proposed as an useful tool for monitoring the increasing degradation of real-world finite-size systems. Moreover, we studied the time fluctuations of the sizes of the biggest clusters of vegetated and nonvegetated cells over the entire range of mortality values. The change of sign in the skewness of the size distributions, occurring at the fragmentation threshold for the biggest vegetation cluster and at the desertification threshold for the nonvegetated cluster, offers new earlywarning signals for desertification. Other new and robust indicators are given by the maxima of the root-mean-square deviation of the distributions, which are attained respectively inside the fragmentation interval, for the vegetated

Signs of impending drought are often vague and result from hydrologic uncertainty. Because of this, determining the appropriate time to enforce water supply restrictions is difficult. This study proposes a drought earlywarning index (DEWI) that can help water resource managers to anticipate droughts so that preparations can be made to mitigate the impact of water shortages. This study employs the expected-deficit-rate of normal water supply conditions as the drought earlywarning index. An annual-use-reservoir-based water supply system in southern Taiwan was selected as the case study. The water supply simulation was based on reservoir storage at the evaluation time and the reservoir inflow series to cope with the actual water supply process until the end of the hydrologic year. A variety of deficits could be realized during different hydrologic years of records and assumptions of initial reservoir storage. These deficits are illustrated using the Average Shortage Rate (ASR) and the value of the ASR, namely the DEWI. The ASR is divided into 5 levels according to 5 deficit-tolerance combinations of each kind of annual demand. A linear regression model and a Neuro-Fuzzy Computing Technique model were employed to estimate the DEWI using selected factors deduced from supply-demand traits and available information, including: rainfall, reservoir inflow and storage data. The chosen methods mentioned above are used to explain a significant index is useful for both model development and decision making. Tests in the Tsengwen-Wushantou reservoir system showed this DEWI to perform very well in adopting the proper mitigation policy at the end of the wet season.

The identification of earlywarning signals for regime shifts in ecosystems is of crucial importance given their impact in terms of economic and social effects. We present here the results of a theoretical study on the desertification transition in semiarid ecosystems under external stress. We performed numerical simulations based on a stochastic cellular automaton model, and we studied the dynamics of the vegetation clusters in terms of percolation theory, assumed as an effective tool for analyzing the geometrical properties of the clusters. Focusing on the role played by the strength of external stresses, measured by the mortality rate m , we followed the progressive degradation of the ecosystem for increasing m , identifying different stages: first, the fragmentation transition occurring at relatively low values of m , then the desertification transition at higher mortality rates, and finally the full desertification transition corresponding to the extinction of the vegetation and the almost complete degradation of the soil, attained at the maximum value of m . For each transition we calculated the spanning probabilities as functions of m and the percolation thresholds according to different spanning criteria. The identification of the different thresholds is proposed as an useful tool for monitoring the increasing degradation of real-world finite-size systems. Moreover, we studied the time fluctuations of the sizes of the biggest clusters of vegetated and nonvegetated cells over the entire range of mortality values. The change of sign in the skewness of the size distributions, occurring at the fragmentation threshold for the biggest vegetation cluster and at the desertification threshold for the nonvegetated cluster, offers new earlywarning signals for desertification. Other new and robust indicators are given by the maxima of the root-mean-square deviation of the distributions, which are attained respectively inside the fragmentation interval, for the

Before and during the El Niño of 2015-2016, regular and frequent application of climate monitoring and seasonal forecasts enabled earlywarning of food insecurity in Africa, Central America, and the Caribbean. As it happened, drought associated with the quasi-El Niño of 2014 had already adversely impacted harvests in Central America, Haiti, and Southern Africa, so the effects of the El Niño of 2015-2016 were especially hard-hitting and particularly devastating to crop conditions and food security. In the case of Ethiopia, 2014 conditions were normal but there were record rainfall deficits in 2015, with consequent crop failure, inadequate forage, and sharply curtailed water availability. Combining such agro-climatological information with knowledge of household economies, livelihood systems, markets & trade, and health & nutrition, FEWS NET constructed scenarios of food insecurity eight months into the future, with monthly updates. These scenarios informed assistance programming by USAID and partners. Overall, FEWS NET estimates that at least 18 million people will be severely food insecure during 2015/16 as a direct result of the impact of El Nino on rainfall. However, in Ethiopia, the contrast with the 1982-1983 El Niño is dramatic; though the two events were climatically similar, the human impacts of the 2015-2016 El Niño are much less, thanks not only to well-functioning earlywarning systems and large scale emergency response, but also improved social safety nets and lack of ongoing armed conflict. In southern Africa, El Nino resulted in extensive failed crops, with some areas of South Africa and Zimbabwe having insufficient rain to plant crops. Remote sensing products provided relevant information to depict the severity of rainfall and vegetation deficits. Likewise, in Central America and the Caribbean (Hispaniola), rainfall deficits were portrayed in the perspective of 30+ years of data.

This paper is a presentation of landslide monitoring, earlywarning and remediation methods recommended for the Polish Carpathians. Instrumentation included standard and automatic on-line measurements with the real-time transfer of data to an Internet web server. The research was funded through EU Innovative Economy Programme and also by the SOPO Landslide Counteraction Project. The landslides investigated were characterized by relatively low rates of the displacements. These ranged from a few millimetres to several centimetres per year. Colluviums of clayey flysch deposits were of a soil-rock type with a very high plasticity and moisture content. The instrumentation consisted of 23 standard inclinometers set to depths of 5-21 m. The starting point of monitoring measurements was in January 2006. These were performed every 1-2 months over the period of 8 years. The measurements taken detected displacements from several millimetres to 40 cm set at a depth of 1-17 m. The modern, on-line monitoring and earlywarning system was installed in May 2010. The system is the first of its kind in Poland and only one of several such real-time systems in the world. The installation was working with the Local Road Authority in Gorlice. It contained three automatic field stations for investigation of landslide parameters to depths of 12-16 m and weather station. In-place tilt transducers and innovative 3D continuous inclinometer systems with sensors located every 0.5 m were used. It has the possibility of measuring a much greater range of movements compared to standard systems. The conventional and real-time data obtained provided a better recognition of the triggering parameters and the control of geohazard stabilizations. The monitoring methods chosen supplemented by numerical modelling could lead to more reliable forecasting of such landslides and could thus provide better control and landslide remediation possibilities also to stabilization works which prevent landslides.

The paper proposes the evaluation of the technical performance of a regional landslide earlywarning system by means of an original approach, called EDuMaP method, comprising three successive steps: identification and analysis of the Events (E), i.e. landslide events and warning events derived from available landslides and warnings databases; definition and computation of a Duration Matrix (DuMa), whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the earlywarning model Performance (P) by means of performance criteria and indicators applied to the duration matrix. During the first step, the analyst takes into account the features of the warning model by means of ten input parameters, which are used to identify and classify landslide and warning events according to their spatial and temporal characteristics. In the second step, the analyst computes a time-based duration matrix having a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The proposed method is based on a framework clearly distinguishing between local and regional landslide earlywarning systems as well as among correlation laws, warning models and warning systems. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warnings data from the municipal earlywarning system operating in Rio de Janeiro (Brazil).

As an integrated observing strategy, the concept of sensorweb for Earth observations is appealing in many aspects. For instance, by increasing the spatial and temporal coverage of observations from space and other vantage points, one can eventually aid in increasing the accuracy of the atmospheric models which are precursor to hurricane track prediction, volcanic eruption forecast, and trajectory path of transcontinental transport of dust, harmful nuclear and chemical plumes. In reality, there is little analysis'available in terms of benefits, costs and optimized set of sensors needed to make these necessary observations. This is a complex problem that must be carefully studied and balanced over many boundaries such as science, defense, earlywarning security, and surveillance. Simplistically, the sensorweb concept from the technological point of view alone has a great appeal in the defense, earlywarning and security applications. In fact, it can be relatively less expensive in per unit cost as opposed to building and deploying it for the scientific use. However, overall observing approach should not be singled out and aligned somewhat . orthogonally to serve a particular need. On the other hand, the sensorweb should be designed and deployed to serve multiple subject areas and customers simultaneously; and can behave as directed measuring systems for both science and operational entities. Sensorweb can be designed to act as expert systems, and/or also provide a dedicated integrated surveillance network. Today, there is no system in the world that is fully integrated in terms of reporting timely multiple hazards warnings, computing the lass of life and property damage estimates, and is also designed to cater to everyone's needs. It is not an easier problem to undertake and more so is not practically solvable. At this time due to some recent events in the world, the scientific community, social scientists, and operational agencies are more cognizant and getting

As an integrated observing strategy, the concept of sensorweb for Earth observations is appealing in many aspects. For instance, by increasing the spatial and temporal coverage of observations from space and other vantage points, one can eventually aid in increasing the accuracy of the atmospheric models which are precursor to hurricane track prediction, volcanic eruption forecast, and trajectory path of transcontinental transport of dust, harmful nuclear and chemical plumes. In reality, there is little analysis'available in terms of benefits, costs and optimized set of sensors needed to make these necessary observations. This is a complex problem that must be carefully studied and balanced over many boundaries such as science, defense, earlywarning, security, and surveillance. Simplistically, the sensorweb concept from the technological point of view alone has a great appeal in the defense, earlywarning and security applications. In fact, it can be relatively less expensive in per unit cost as opposed to building and deploying it for the scientific use. However, overall observing approach should not be singled out and aligned somewhat orthogonally to serve a particular need. On the other hand, the sensorweb should be designed and deployed to serve multiple subject areas and customers simultaneously; and can behave as directed measuring systems for both science and operational entities. Sensorweb can be designed to act as expert systems, and/or also provide a dedicated integrated surveillance network. Today, there is no system in the world that is fully integrated in terms of reporting timely multiple hazards warnings, computing the loss of life and property damage estimates, and is also designed to cater to everyone's needs. It is not an easier problem to undertake and more so is not practically solvable. At this time due to some recent events in the world, the scientific community, social scientists, and operational agencies are more cognizant and getting

Introduction Within the GITEWS (German Indonesian Tsunami EarlyWarning System) project a near real-time GNSS processing system has been developed, which analizes on- and offshore measured GNSS data. It is the first system of its kind that was integrated into an operational tsunami earlywarning system. (Indonesian Tsunami EarlyWarning Centre INATEWS, inaugurated at BMKG Jakarta on November, 11th 2008) Brief system description The GNSS data to be processed are received from sensors (GNSS antenna and receiver) installed on buoys, at tide gauges and as real-time reference stations (RTR stations), either stand-alone or co-located with seismic sensors. The GNSS data are transmitted to the warning centre in real-time as a stream (RTR stations) or file-based and are processed in a near real-time data processing chain. The fully automatized system uses the BERNESE GPS software as processing core. Kinematic coordinate timeseries with a resolution of 1 Hz (landbased stations) and 1/3 Hz (buoys) are estimated every five minutes. In case of a recently occured earthquake the processing interval decreases from five to two minutes. All stations are processed with the relative technique (baseline-technique) using GITEWS-stations and stations available via IGS as reference. The most suitable reference stations are choosen by querying a database where continiously monitored quality data of GNSS observations are stored. In case of an earthquake at least one reference station should be located on a different tectonic plate to ensure that relative movements can be detected. The primary source for satellite orbit information is the IGS IGU product. If this source is not available for any reason, the system switches automatically to other orbit sources like CODE products or broadcast ephemeris data. For sensors on land the kinematic coordinates are used to detect deviations from their normal, mean coordinates. The deviations or so called displacements are indicators for land mass

An earlywarning system is an intentional process whereby school personnel collectively analyze student data to monitor students at risk of falling off track for graduation and to provide the interventions and resources to intervene. We studied the process of monitoring the earlywarning indicators and implementing interventions to ascertain…

The EarlyWarning System is a pheromone-based trapping system used to detect outbreaks of Douglas-fir tussock moth (DFTM, Orgyia pseudotsugata) in the western United States. Millions of acres are susceptible to DFTM defoliation, but EarlyWarning System monitoring focuses attention only on the relatively limited areas where outbreaks may be...

This paper describes the development of the Flash Flood Manager, abbreviated as FlaFloM. The Flash Flood Manager is an earlywarning system for flash floods which is developed under the EU LIFE project FlaFloM. It is applied to Wadi Watier located in the Sinai peninsula (Egypt) and discharges in the Red Sea at the local economic and tourist hub of Nuweiba city. FlaFloM consists of a chain of four modules: 1) Data gathering module, 2) Forecasting module, 3) Decision support module or DSS and 4) Warning module. Each module processes input data and consequently send the output to the following module. In case of a flash flood emergency, the final outcome of FlaFloM is a flood warning which is sent out to decision-makers. The ‘data gathering module’ collects input data from different sources, validates the input, visualise data and exports it to other modules. Input data is provided ideally as water stage (h), discharge (Q) and rainfall (R) through real-time field measurements and external forecasts. This project, however, as occurs in many arid flash flood prone areas, was confronted with a scarcity of data, and insufficient insight in the characteristics that release a flash flood. Hence, discharge and water stage data were not available. Although rainfall measurements are available through classical off line rain gauges, the sparse rain gauges network couldn’t catch the spatial and temporal characteristics of rainfall events. To overcome this bottleneck, we developed rainfall intensity raster maps (mm/hr) with an hourly time step and raster cell of 1*1km. These maps are derived through downscaling from two sources of global instruments: the weather research and forecasting model (WRF) and satellite estimates from the Tropical Rainfall Measuring Mission (TRMM). The ‘forecast module’ comprises three numerical models that, using data from the gathering module performs simulations on command: a rainfall-runoff model, a river flow model, and a flood model. A

Recently, Global Navigation Satellite System (GNSS) has been used for rapid earthquake source inversion towards tsunami earlywarning. In practice, two approaches, i.e., static finite source inversion based on permanent co-seismic offsets and kinematic finite source inversion using high-rate (>= 1 Hz) co-seismic displacement waveforms, are often employed to fulfill the task. The static inversion is relatively easy to be implemented and does not require additional constraints on rupture velocity, duration, and temporal variation. However, since most GNSS receivers are deployed onshore locating on one side of the subduction fault, there is very limited resolution on near-trench fault slip using GNSS in static finite source inversion. On the other hand, the high-rate GNSS displacement waveforms, which contain the timing information of earthquake rupture explicitly and static offsets implicitly, have the potential to improve near-trench resolution by reconciling with the depth-dependent megathrust rupture behaviors. In this contribution, we assess the performance of rapid kinematic finite source inversion using high-rate GNSS by three selected historical tsunamigenic cases: the 2010 Mentawai, 2011 Tohoku and 2015 Illapel events. With respect to the 2010 Mentawai case, it is a typical tsunami earthquake with most slip concentrating near the trench. The static inversion has little resolution there and incorrectly puts slip at greater depth (>10km). In contrast, the recorded GNSS displacement waveforms are deficit in high-frequency energy, the kinematic source inversion recovers a shallow slip patch (depth less than 6 km) and tsunami runups are predicted quite reasonably. For the other two events, slip from kinematic and static inversion show similar characteristics and comparable tsunami scenarios, which may be related to dense GNSS network and behavior of the rupture. Acknowledging the complexity of kinematic source inversion in real-time, we adopt the back

The disease earlywarning system (DEWS) was introduced in the immediate aftermath of the 2005 earthquake in Pakistan, with the objective to undertake prompt investigation and mitigation of disease outbreaks. The DEWS network was replicated successfully during subsequent flood and earthquake disasters as well as during the 2008-09 internally displaced persons' crisis. DEWS-generated alerts, prompt investigations and timely responses had an effective contribution to the control of epidemics. Through DEWS, 1360 reported alerts during 2005-09 averted the risk of disease outbreaks through pre-emptive necessary measures, while the 187 confirmed outbreaks were effectively controlled. In the aftermath of the disasters, DEWS technology also facilitated the development of a disease-surveillance system that became an integral part of the district health system. This study aims to report the DEWS success and substantiate its lead role as a priority emergency health response intervention.

Earlywarning systems are an important tool for natural disaster mitigation practices, especially for flooding events. Warnings rely on near-future forecasts to provide time to take preventive actions before a flood occurs, thus reducing potential losses. However, on top of the technical capacities, successful warnings require an efficient coordination and communication among a range of different actors and stakeholders. The complexity of integrating the technical and social spheres of warning systems has, however, resulted in system designs neglecting a number of important aspects such as social awareness of floods thus leading to suboptimal results. A better understanding of the interactions and feedbacks among the different elements of earlywarning systems is therefore needed to improve their efficiency and therefore social resilience. When designing an earlywarning system two important decisions need to be made regarding (i) the hazard magnitude at and from which a warning should be issued and (ii) the degree of confidence required for issuing a warning. The first decision is usually taken based on the social vulnerability and climatic variability while the second one is related to the performance (i.e. accuracy) of the forecasting tools. Consequently, by estimating the vulnerability and the accuracy of the forecasts, these two variables can be optimized to minimize the costs and losses. Important parameters with a strong influence on the efficiency of warning systems such as social awareness are however not considered in their design. In this study we present a theoretical exploration of the impact of social awareness on the design of earlywarning systems. For this purpose we use a definition of social memory of flood events as a proxy for flood risk awareness and test its effect on the optimization of the warning system design variables. Understanding the impact of social awareness on warning system design is important to make more robust warnings that can

There are several secondary care earlywarning scores which alert for severe illness including sepsis. None are specifically adjusted for primary care. A Primary Health EarlyWarning Score (PHEWS) is proposed which incorporates practical parameters from both secondary and primary care.

The main focus of the paper is to present a flood and landslide earlywarning system, named HEWS (Hydrohazards EarlyWarning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall-streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall-runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall-runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002-2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce

Ecosystems on the verge of major reorganization—regime shift—may exhibit declining resilience, which can be detected using a collection of generic statistical tests known as earlywarning signals (EWSs). This study explores whether EWSs anticipated human population collapse during the European Neolithic. It analyzes recent reconstructions of European Neolithic (8–4 kya) population trends that reveal regime shifts from a period of rapid growth following the introduction of agriculture to a period of instability and collapse. We find statistical support for EWSs in advance of population collapse. Seven of nine regional datasets exhibit increasing autocorrelation and variance leading up to collapse, suggesting that these societies began to recover from perturbation more slowly as resilience declined. We derive EWS statistics from a prehistoric population proxy based on summed archaeological radiocarbon date probability densities. We use simulation to validate our methods and show that sampling biases, atmospheric effects, radiocarbon calibration error, and taphonomic processes are unlikely to explain the observed EWS patterns. The implications of these results for understanding the dynamics of Neolithic ecosystems are discussed, and we present a general framework for analyzing societal regime shifts using EWS at large spatial and temporal scales. We suggest that our findings are consistent with an adaptive cycling model that highlights both the vulnerability and resilience of early European populations. We close by discussing the implications of the detection of EWS in human systems for archaeology and sustainability science. PMID:27573833

Ecosystems on the verge of major reorganization-regime shift-may exhibit declining resilience, which can be detected using a collection of generic statistical tests known as earlywarning signals (EWSs). This study explores whether EWSs anticipated human population collapse during the European Neolithic. It analyzes recent reconstructions of European Neolithic (8-4 kya) population trends that reveal regime shifts from a period of rapid growth following the introduction of agriculture to a period of instability and collapse. We find statistical support for EWSs in advance of population collapse. Seven of nine regional datasets exhibit increasing autocorrelation and variance leading up to collapse, suggesting that these societies began to recover from perturbation more slowly as resilience declined. We derive EWS statistics from a prehistoric population proxy based on summed archaeological radiocarbon date probability densities. We use simulation to validate our methods and show that sampling biases, atmospheric effects, radiocarbon calibration error, and taphonomic processes are unlikely to explain the observed EWS patterns. The implications of these results for understanding the dynamics of Neolithic ecosystems are discussed, and we present a general framework for analyzing societal regime shifts using EWS at large spatial and temporal scales. We suggest that our findings are consistent with an adaptive cycling model that highlights both the vulnerability and resilience of early European populations. We close by discussing the implications of the detection of EWS in human systems for archaeology and sustainability science.

High-frequency (HF) ocean radars give a unique capability to deliver simultaneous wide area measurements of ocean surface current fields and sea state parameters far beyond the horizon. The WERA® ocean radar system is a shore-based remote sensing system to monitor ocean surface in near real-time and at all-weather conditions up to 300 km offshore. Tsunami induced surface currents cause increasing orbital velocities comparing to normal oceanographic situation and affect the measured radar spectra. The theoretical approach about tsunami influence on radar spectra showed that a tsunami wave train generates a specific unusual pattern in the HF radar spectra. While the tsunami wave is approaching the beach, the surface current pattern changes slightly in deep water and significantly in the shelf area as it was shown in theoretical considerations and later proved during the 2011 Japan tsunami. These observed tsunami signatures showed that the velocity of tsunami currents depended on a tsunami wave height and bathymetry. The HF ocean radar doesn't measure the approaching wave height of a tsunami; however, it can resolve the surface current velocity signature, which is generated when tsunami reaches the shelf edge. This strong change of the surface current can be detected by a phased-array WERA system in real-time; thus the WERA ocean radar is a valuable tool to support Tsunami EarlyWarning Systems (TEWS). Based on real tsunami measurements, requirements for the integration of ocean radar systems into TEWS are already defined. The requirements include a high range resolution, a narrow beam directivity of phased-array antennas and an accelerated data update mode to provide a possibility of offshore tsunami detection in real-time. The developed software package allows reconstructing an ocean surface current map of the area observed by HF radar based on the radar power spectrum processing. This fact gives an opportunity to issue an automated tsunami identification message

The scientific case for the clear and present danger of global warming has been unassailable at least since the release of the Charney Report more than thirty years ago, if not longer. While prompt action to begin decarbonizing energy systems could still head off much of the potential warming, it is distinctly possible that emissions will continue unabated in the coming decades, leading to a doubling or more of pre-industrial carbon dioxide concentrations. At present, we are in the unenviable position of not even knowing how bad things will get if this scenario comes to pass, because of the uncertainty in climate sensitivity. If climate sensitivity is high, then the consequences will be dire, perhaps even catastrophic. As the world continues to warm in response to continued carbon dioxide emissions, will we at least be able to monitor the climate and provide an earlywarning that the planet is on a high-sensitivity track, if such turns out to be the case? At what point will we actually know the climate sensitivity? It has long been recognized that the prime contributor to uncertainty in climate sensitivity is uncertainty in cloud feedbacks. Study of paleoclimate and climate of the past century has not been able to resolve which models do cloud feedback most correctly, because of uncertainties in radiative forcing. In this talk, I will discuss monitoring requirements, and analysis techniques, that might have the potential to determine which climate models most faithfully represent climate feedbacks, and thus determine which models provide the best estimate of climate sensitivity. The endeavor is complicated by the distinction between transient climate response and equilibrium climate sensitivity. I will discuss the particular challenges posed by this issue, particularly in light of recent indications that the pattern of ocean heat storage may lead to different cloud feedbacks in the transient warming stage than apply once the system has reached equilibrium. Apart

Many earth systems have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been described, among others, for climate, vegetation, animal populations, and geomorphology. Predicting the timing of critical transitions before they are reached is of importance because of the large impact on nature and society associated with the transition. However, it is notably difficult to predict the timing of a transition. This is because the state variables of the system show little change before the threshold is reached. As a result, the precision of field observations is often too low to provide predictions of the timing of a transition. A possible solution is the use of spatio-temporal patterns in state variables as leading indicators of a transition. It is becoming clear that the critically slowing down of a system causes spatio-temporal autocorrelation and variance to increase before the transition. Thus, spatio-temporal patterns are important candidates for early-warning signals. In this research we will show that these early-warning signals also exist in geomorphological systems. We consider a modelled vegetation-soil system under a gradually increasing grazing pressure causing an abrupt shift towards extensive soil degradation. It is shown that changes in spatio-temporal patterns occur well ahead of this catastrophic transition. A distributed model describing the coupled processes of vegetation growth and geomorphological denudation is adapted. The model uses well-studied simple process representations for vegetation and geomorphology. A logistic growth model calculates vegetation cover as a function of grazing pressure and vegetation growth rate. Evolution of the soil thickness is modelled by soil creep and wash processes, as a function of net rain reaching the surface. The vegetation and soil system are coupled by 1) decreasing vegetation growth with decreasing soil thickness and 2) increasing soil wash with

The Famine EarlyWarning Systems Network (FEWS NET) makes quantitative estimates of food insecure populations, and identifies the places and periods during which action must be taken to assist them. Subsistence agriculture and pastoralism are the predominant livelihood systems being monitored, and they are especially drought-sensitive. At the same time, conventional climate observation networks in developing countries are often sparse and late in reporting. Consequently, remote sensing has played a significant role since FEWS NET began in 1985. Initially there was heavy reliance on vegetation index imagery from AVHRR to identify anomalies in landscape greenness indicative of drought. In the latter part of the 1990s, satellite rainfall estimates added a second, independent basis for identification of drought. They are used to force crop water balance models for the principal rainfed staple crops in twenty FEWS NET countries. Such models reveal seasonal moisture deficits associated with yield reduction on a spatially continuous basis. In 2002, irrigated crops in southwest Asia became a concern, and prompted the implementation of a gridded energy balance model to simulate the seasonal mountain snow pack, the main source of irrigation water. MODIS land surface temperature data are also applied in these areas to directly estimate actual seasonal evapotranspiration on the irrigated lands. The approach reveals situations of reduced irrigation water supply and crop production due to drought. The availability of MODIS data after 2000 also brought renewed interest in vegetation index imagery. MODIS NDVI data have proven to be of high quality, thanks to significant spectral and spatial resolution improvements over AVHRR. They are vital to producing rapid harvest assessments for drought-impacted countries in Africa and Asia. The global food crisis that emerged in 2008 has led to expansion of FEWS NET monitoring to over 50 additional countries. Unlike previous practice, these

The panoramic splendor and complexity of mountain environments have inspired and challenged humans for centuries. These areas have been variously perceived as physical structures to be conquered, as sites of spiritual inspiration, and as some of the last untamed natural places on Earth. In our time, the perception that "mountains are forever" may provide solace to those seeking stability in a rapidly changing world. However, changes in the hydrology and in the abundance and species composition of the native flora and fauna of mountain ecosystems are potential bellwethers of global change, because these systems have a propensity to amplify environmental changes within specific portions of this landscape. Mountain areas are thus sentinels of climate change. We are seeing effects today in case histories I present from the Himalaya's, Andes, Alps, and Rocky Mountains. Furthermore, these ecosystem changes are occurring in mountain areas before they occur in downstream ecosystems. Thus, mountains are earlywarning indicators of perturbations such as climate change. The sensitivity of mountain ecosystems begs for enhanced protection and worldwide protection. Our understanding of the processes that control mountain ecosystems—climate interactions, snowmelt runoff, biotic diversity, nutrient cycling—is much less developed compared to downstream ecosystems where human habitation and development has resulted in large investments in scientific knowledge to sustain health and agriculture. To address these deficiencies, I propose the formation of an international mountain research consortium.

Landslide earlywarning systems (EWSs) have to be implemented in areas with large risk for populations or infrastructures when classical structural remediation measures cannot be set up. This paper aims to gather experiences of existing landslide EWSs, with a special focus on practical requirements (e.g., alarm threshold values have to take into account the smallest detectable signal levels of deployed sensors before being established) and specific issues when dealing with system implementations. Within the framework of the SafeLand European project, a questionnaire was sent to about one-hundred institutions in charge of landslide management. Finally, we interpreted answers from experts belonging to 14 operational units related to 23 monitored landslides. Although no standard requirements exist for designing and operating EWSs, this review highlights some key elements, such as the importance of pre-investigation work, the redundancy and robustness of monitoring systems, the establishment of different scenarios adapted to gradual increasing of alert levels, and the necessity of confidence and trust between local populations and scientists. Moreover, it also confirms the need to improve our capabilities for failure forecasting, monitoring techniques and integration of water processes into landslide conceptual models.

The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies. PMID:24285089

Massive forest mortality was observed in California during the most recent drought. Owing to complex interactions of physiological mechanisms under stress, prediction of climate-induced forest mortality using dynamic global vegetation models remains fraught with uncertainty. Given that forest ecosystems approaching mortality tend to exhibit reduction in resilience, we evaluate the time-varying resilience from time series of satellite images to detect earlywarning signals (EWSs) of mortality. Four metrics of EWSs are used: (1) low greenness, (2) high empirical autocorrelation of greenness, (3) high autocorrelation inferred using a Bayesian dynamic linear model considering the influence of seasonality and climate conditions, and (4) low recovery rate inferred from the drift term in the Langevin equation describing stochastic dynamics. Spatial accuracy and lead-time of these EWSs are evaluated by comparing the EWSs against observed mortality from aerial surveys conducted by the US Forest Service. Our results show that most forested areas in California that underwent mortality exhibit a EWS with a lead time of three months to two years ahead of observed mortality. Notably, EWS is also detected in some areas without mortality, suggesting reduced resilience during drought. Furthermore, the influence of the previous drought (2007-2009) may have propagated into the recent drought (2012-2016) through reduced resilience, hence contributing to the massive forest mortality observed recently. Methodologies developed in this study for detection of EWS will improve the near-term predictability of forest mortality, thus providing crucial information for forest and water resource management.

Rapid changes in state have been documented for many of Earth's ecosystems. Despite a growing toolbox of methods for detecting declining resilience or earlywarning indicators (EWIs) of ecosystem transitions, these methods have rarely been evaluated in whole-ecosystem trials using reference ecosystems. In this study, we experimentally tested EWIs of cyanobacteria blooms based on changes in the spatial structure of a lake. We induced a cyanobacteria bloom by adding nutrients to an experimental lake and mapped fine-resolution spatial patterning of cyanobacteria using a mobile sensor platform. Prior to the bloom, we detected theoretically predicted spatial EWIs based on variance and spatial autocorrelation, as well as a new index based on the extreme values. Changes in EWIs were not discernible in an unenriched reference lake. Despite the fluid environment of a lake where spatial heterogeneity driven by biological processes may be overwhelmed by physical mixing, spatial EWIs detected an approaching bloom suggesting the utility of spatial metrics for signaling ecological thresholds.

Cotton production plays an important role in Hebei. It straightly influences cotton farmers’ life, agricultural production and national economic development as well. In recent years, due to cotton production frequently fluctuating, two situations, “difficult selling cotton” and “difficult buying cotton” have alternately occurred, and brought disadvantages to producers, businesses and national finance. Therefore, it is very crucial to research the earlywarning of cotton production for solving the problem of cotton production’s frequent fluctuation and ensuring the cotton industry’s sustainable development. This paper founds a signal lamp model of earlywarning through employing time-difference correlation analysis method to select early-warning indicators and statistical analysis method associated with empirical analysis to determine early-warning limits. Finally, it not only obtained warning conditions of cotton production from 1993 to 2006 and forecast 2007’s condition, but also put forward corresponding countermeasures to prevent cotton production from fluctuating. Furthermore, an early-warning software of cotton production is completed through computer programming on the basis of the earlywarning model above.

The Black Sea area is liable to tsunamis generation and the statistics show that more than twenty tsunamis have been observed in the past. The last tsunami was observed on 31st of March 1901 in the western part of the Black Sea, in the Shabla area. An earthquake of magnitude generated at a depth of 15 km below the sea level , triggered tsunami waves of 5 m height and material losses as well. The oldest tsunami ever recorded close to the Romanian shore-line dates from year 104. This paper emphasises the participation of The National Institute for Earth Physics (NIEP) to the development of a tsunami warning system for the western cost of the Black Sea. In collaboration with the National Institute for Marine Geology and Geoecology (GeoEcoMar), the Institute of Oceanology and the Geological Institute, the last two belonging to the Bulgarian Academy of Science, NIEP has participated as partner, to the cross-border project "Set-up and implementation of key core components of a regional early-warning system for marine geohazards of risk to the Romanian-Bulgarian Black Sea coastal area - MARINEGEOHAZARDS", coordinated by GeoEcoMar. The main purpose of the project was the implementation of an integrated early-warning system accompanied by a common decision-support tool, and enhancement of regional technical capability, for the adequate detection, assessment, forecasting and rapid notification of natural marine geohazards for the Romanian-Bulgarian Black Sea cross-border area. In the last years, NIEP has increased its interest on the marine related hazards, such as tsunamis and, in collaboration with other institutions of Romania, is acting to strengthen the cooperation and data exchanges with institutions from the Black Sea surrounding countries which already have tsunami monitoring infrastructures. In this respect, NIEP has developed a coastal network for marine seismicity, by installing three new seismic stations in the coastal area of the Black Sea, Sea Level Sensors

Tsunami warning systems (TWS) have the final goal to launch a reliable alert of an incoming dangerous tsunami to coastal population early enough to allow people to flee from the shore and coastal areas according to some evacuation plans. In the last decade, especially after the catastrophic 2004 Boxing Day tsunami in the Indian Ocean, much attention has been given to filling gaps in the existing TWSs (only covering the Pacific Ocean at that time) and to establishing new TWSs in ocean regions that were uncovered. Typically, TWSs operating today work only on earthquake-induced tsunamis. TWSs estimate quickly earthquake location and size by real-time processing seismic signals; on the basis of some pre-defined "static" procedures (either based on decision matrices or on pre-archived tsunami simulations), assess the tsunami alert level on a large regional scale and issue specific bulletins to a pre-selected recipients audience. Not unfrequently these procedures result in generic alert messages with little value. What usually operative TWSs do not do, is to compute earthquake focal mechanism, to calculate the co-seismic sea-floor displacement, to assess the initial tsunami conditions, to input these data into tsunami simulation models and to compute tsunami propagation up to the threatened coastal districts. This series of steps is considered nowadays too time consuming to provide the required timely alert. An equivalent series of steps could start from the same premises (earthquake focal parameters) and reach the same result (tsunami height at target coastal areas) by replacing the intermediate steps of real-time tsunami simulations with proper selection from a large archive of pre-computed tsunami scenarios. The advantage of real-time simulations and of archived scenarios selection is that estimates are tailored to the specific occurring tsunami and alert can be more detailed (less generic) and appropriate for local needs. Both these procedures are still at an

This study aims to development a regional susceptibility model and warning threshold as well as the establishment of earlywarning system in order to prevent and reduce the losses caused by rainfall-induced shallow landslides in Taiwan. For the purpose of practical application, Taiwan is divided into nearly 185,000 slope units. The susceptibility and warning threshold of each slope unit were analyzed as basic information for disaster prevention. The geological characteristics, mechanism and the occurrence time of landslides were recorded for more than 900 cases through field investigation and interview of residents in order to discuss the relationship between landslides and rainfall. Logistic regression analysis was performed to evaluate the landslide susceptibility and an I3-R24 rainfall threshold model was proposed for the earlywarning of landslides. The validations of recent landslide cases show that the model was suitable for the warning of regional shallow landslide and most of the cases can be warned 3 to 6 hours in advanced. We also propose a slope unit area weighted method to establish local rainfall threshold on landslide for vulnerable villages in order to improve the practical application. Validations of the local rainfall threshold also show a good agreement to the occurrence time reported by newspapers. Finally, a web based "Rainfall-induced Landslide EarlyWarning System" is built and connected to real-time radar rainfall data so that landslide real-time warning can be achieved. Keywords: landslide, susceptibility analysis, rainfall threshold

Over the past year few years, an international collaboration has developed a pilot project under the auspices of Committee on Earth Observation Satellite (CEOS) Disasters team. The overall team consists of civilian satellite agencies. For this pilot effort, the development team consists of NASA, Canadian Space Agency, Univ. of Maryland, Univ. of Colorado, Univ. of Oklahoma, Ukraine Space Research Institute and Joint Research Center(JRC) for European Commission. This development team collaborates with regional , national and international agencies to deliver end-to-end disaster coverage. In particular, the team in collaborating on this effort with the Namibia Department of Hydrology to begin in Namibia . However, the ultimate goal is to expand the functionality to provide earlywarning over the South Africa region. The initial collaboration was initiated by United Nations Office of Outer Space Affairs and CEOS Working Group for Information Systems and Services (WGISS). The initial driver was to demonstrate international interoperability using various space agency sensors and models along with regional in-situ ground sensors. In 2010, the team created a preliminary semi-manual system to demonstrate moving and combining key data streams and delivering the data to the Namibia Department of Hydrology during their flood season which typically is January through April. In this pilot, a variety of moderate resolution and high resolution satellite flood imagery was rapidly delivered and used in conjunction with flood predictive models in Namibia. This was collected in conjunction with ground measurements and was used to examine how to create a customized flood earlywarning system. During the first year, the team made use of SensorWeb technology to gather various sensor data which was used to monitor flood waves traveling down basins originating in Angola, but eventually flooding villages in Namibia. The team made use of standardized interfaces such as those articulated

By considering drought as a hazard, drought types are classified into three categories, namely meteorological or climatological, agrometeorological or agricultural and hydrological drought and as a fourth class the socioeconomic impacts can be considered. This paper addresses agrometeorological drought affecting agriculture within the risk management framework. Risk management consists of risk assessment, as well as a feedback on the adopted risk reduction measures. And risk assessment comprises three distinct steps, namely risk identification, risk estimation and risk evaluation. This paper deals with the quantification and monitoring of agrometeorological drought, which constitute part of risk identification. For the quantitative assessment of agrometeorological or agricultural drought, as well as the computation of spatiotemporal features, one of the most reliable and widely used indices is applied, namely the Vegetation Health Index (VHI). The computation of VHI is based on satellite data of temperature and the Normalized Difference Vegetation Index (NDVI). The spatiotemporal features of drought, which are extracted from VHI are: areal extent, onset and end time, duration and severity. In this paper, a 20-year (1981-2001) time series of NOAA/AVHRR satellite data is used, where monthly images of VHI are extracted. Application is implemented in Thessaly, which is the major agricultural region of Greece characterized by vulnerable and drought-prone agriculture. The results show that every year there is a seasonal agrometeorological drought with a gradual increase in the areal extent and severity with peaks appearing usually during the summer. Drought monitoring is conducted by monthly remotely sensed VHI images. Drought earlywarning is developed using empirical relationships of severity and areal extent. In particular, two second-order polynomials are fitted, one for low and the other for high severity drought, respectively. The two fitted curves offer a seasonal

Accidents hardly ever happen without warning. The combination, or sequence, of failures and mistakes that cause an accident may indeed be unique but the individual failures and mistakes rarely are. In the USA in 1974 the crews on two different aircraft misunderstood the same aeronautical chart and descended towards their destination dangerously early towards a mountain. The first crew were in good weather conditions and could see the mountain and resolved their misinterpretation of the chart. The second crew six weeks later were not so lucky. In cloud they had no clues to point out their mistake nor the presence of the mountain. The resulting crash and the ensuing inquiry, which brought to light the previous incident, shocked the country but gave it the impetus to instigate a safety reporting system. This system eventually became the NASA's Aviation Safety Reporting System (ASRS). The programme collects incident reports from pilots, controllers, mechanics, cabin attendants and many others involved in aviation operations. By disseminating this safety information the ASRS has helped enormously to give US airlines and airspace the highest safety standards. Accident prevention is a goal sought by everyone in the aviation industry and establishing effective incident reporting programmes can go a long way toward achieving that goal. This article will describe the steps and issues required to establish an incident reporting system. The authors summarize the lessons learned from the ASRS, now in its twentieth year of operation and from the Confidential Human Factors Reporting (HER) Programme run by British Airways, an airline that is a recognized world leader in safety reporting and analysis. The differences between government and airline operation of confidential safety reporting systems will be addressed.

Earlywarning of the timing and locations of malaria epidemics can facilitate the targeting of resources for prevention and emergency response. In response to this need, we are developing the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. EPIDEMIA incorporates software for capturing, processing, and integrating environmental and epidemiological data from multiple sources; data assimilation techniques that continually update models and forecasts; and a web-based interface that makes the resulting information available to public health decision makers. The system will enable forecasts that incorporate lagged responses to environmental risk factors as well as information about recent trends in malaria cases. Because the egg, larval, and pupal stages of mosquito development occur in aquatic habitats, information about the spatial and temporal distributions of stagnant water bodies is critical for modeling malaria risk. Potential sources of hydrological data include satellite-derived rainfall estimates, evapotranspiration (ET) calculated using a simplified surface energy balance model, and estimates of soil moisture and fractional water cover from passive microwave radiometry. We used partial least squares regression to analyze and visualize seasonal patterns of these variables in relation to malaria cases using data from 49 districts in the Amhara region of Ethiopia. Seasonal patterns of rainfall were strongly associated with the incidence and seasonality of malaria across the region, and model fit was improved by the addition of remotely-sensed ET and soil moisture variables. The results highlight the importance of remotely-sensed hydrological data for modeling malaria risk in this region and emphasize the value of an ensemble approach that utilizes multiple sources of information about precipitation and land surface wetness. These variables will be incorporated into the forecasting models at

The purpose of this paper is to evaluate staff opinion on the impact of the National EarlyWarning Score (NEWS) system on surgical wards. In 2012, the NEWS system was introduced to Irish hospitals on a phased basis as part of a national clinical programme in acute care. A modified established questionnaire was given to surgical nursing staff, surgical registrars, surgical senior house officers and surgical interns for completion six months following the introduction of the NEWS system into an Irish university hospital. Amongst the registrars, 89 per cent were unsure if the NEWS system would improve patient care. Less than half of staff felt consultants and surgical registrars supported the NEWS system. Staff felt the NEWS did not correlate well clinically with patients within the first 24 hours (Day zero) post-operatively. Furthermore, 78-85 per cent of nurses and registrars felt a rapid response team should be part of the escalation protocol. Senior medical staff were not convinced that the NEWS system may improve patient care. Appropriate audit proving a beneficial impact of the NEWS system on patient outcome may be essential in gaining support from senior doctors. Deficiencies with the system were also observed including the absence of a rapid response team as part of the escalation protocol and a lack of concordance of the NEWS in patients Day zero post-operatively. These issues should be addressed moving forward. Appropriate audit of the impact of the NEWS system on patient outcome may be pertinent to obtain the support from senior doctors. Deficiencies with the system were also observed including the absence of a rapid response team as part of the escalation protocol and a lack of concordance of the NEWS in patients Day zero post-operatively. These issues should be addressed moving forward.

Avalanching glacier instabilities are gravity-driven rupture phenomena that might cause major disasters, especially when they are at the origin of a chain of processes. Reliably forecasting such events combined with a timely evacuation of endangered inhabited areas often constitute the most efficient action. Recently, considerable efforts in monitoring, analyzing, and modeling such phenomena have led to significant advances in destabilization process understanding, improving earlywarning perspectives. The purpose of this paper is to review the recent progress in this domain. Three different types of instabilities can be identified depending on the thermal properties of the ice/bed interface. If cold (1), the maturation of the rupture is associated with a typical time evolution of surface velocities and passive seismic activity. A prediction of the final break off is possible using these precursory signs. For the two other types, water plays a key role in the development of the instability. If the ice/bed interface is partly temperate (2), the presence of meltwater may reduce the basal resistance, which promotes the instability. No clear and easily detectable precursory signs are known in this case, and the only way to infer any potential instability is to monitor the temporal evolution of the thermal regime. The last type of instability (3) concerns steep temperate glacier tongues switching for several days/weeks during the melting season into a so-called "active phase" followed in rare cases by a major break-off event. Although the prediction of such events is still far from being achievable, critical conditions promoting the final instability can be identified.

Food security in countries at risk is monitored by U.S. Agency for International Development (USAID) through its Famine EarlyWarning Systems Network (FEWS NET) using many methods including Moderate Resolution Imaging Spectroradiometer (MODIS) data processed by U.S. Geological Survey (USGS) into eMODIS Normalized Difference Vegetation Index (NDVI) products. Near-real time production is used comparatively with trends derived from the eMODIS archive to operationally monitor vegetation anomalies indicating threatened cropland and rangeland conditions. eMODIS production over Central America and the Caribbean (CAMCAR) began in 2009, and processes 10-day NDVI composites every 5 days from surface reflectance inputs produced using predicted spacecraft and climatology information at Land and Atmosphere Near real time Capability for Earth Observing Systems (EOS) (LANCE). These expedited eMODIS composites are backed by a parallel archive of precision-based NDVI calculated from surface reflectance data ordered through Level 1 and Atmosphere Archive and Distribution System (LAADS). Success in the CAMCAR region led to the recent expansion of eMODIS production to include Africa in 2010, and Central Asia in 2011. Near-real time 250-meter products are available for each region on the last day of an acquisition interval (generally before midnight) from an anonymous file transfer protocol (FTP) distribution site (ftp://emodisftp.cr.usgs.gov/eMODIS). The FTP site concurrently hosts the regional historical collections (2000 to present) which are also searchable using the USGS Earth Explorer (http://edcsns17.cr.usgs.gov/NewEarthExplorer). As eMODIS coverage continues to grow, these geographically gridded, georeferenced tagged image file format (GeoTIFF) NDVI composites increase their utility as effective tools for operational monitoring of near-real time vegetation data against historical trends.

Predicting population declines is a key challenge in the face of global environmental change. Abundance-based earlywarning signals have been shown to precede population collapses; however, such signals are sensitive to the low reliability of abundance estimates. Here, using historical data on whales harvested during the 20th century, we demonstrate that earlywarning signals can be present not only in the abundance data, but also in the more reliable body size data of wild populations. We show that during the period of commercial whaling, the mean body size of caught whales declined dramatically (by up to 4 m over a 70-year period), leading to earlywarning signals being detectable up to 40 years before the global collapse of whale stocks. Combining abundance and body size data can reduce the length of the time series required to predict collapse, and decrease the chances of false positive earlywarning signals.

... requires quarterly reporting of earlywarning information: Production information; information on incidents... manufacturers, and other equipment manufacturers) and the annual production of the entity. The EWR information... vehicle type as part of [[Page 55608

Introduction Technical outreach - distinct from general-interest and K-12 educational outreach - for volcanic hazards is aimed at providing usable scientific information about potential or ongoing volcanic activity to public officials, businesses, and individuals in support of their response, preparedness, and mitigation efforts. Within the context of a National Volcano EarlyWarning System (NVEWS) (Ewert et al., 2005), technical outreach is a critical process, transferring the benefits of enhanced monitoring and hazards research to key constituents who have to initiate actions or make policy decisions to lessen the hazardous impact of volcanic activity. This report discusses recommendations of the Technical-Information Products Working Group convened in 2006 as part of the NVEWS planning process. The basic charge to the Working Group was to identify a web-based, volcanological 'product line' for NVEWS to meet the specific hazard-information needs of technical users. Members of the Working Group were: *Marianne Guffanti (Chair), USGS, Reston VA *Steve Brantley, USGS, Hawaiian Volcano Observatory HI *Peter Cervelli, USGS, Alaska Volcano Observatory, Anchorage AK *Chris Nye, Division of Geological and Geophysical Surveys and Alaska Volcano Observatory, Fairbanks AK *George Serafino, National Oceanic and Atmospheric Administration, Camp Springs MD *Lee Siebert, Smithsonian Institution, Washington DC *Dina Venezky, USGS, Volcano Hazards Team, Menlo Park CA *Lisa Wald, USGS, Earthquake Hazards Program, Golden CO

Recognition and timely action around “warning signs” of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that earlywarning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that earlywarning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for earlywarning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating earlywarning systems for bipolar disorder with a focus on pragmatic considerations. PMID:27604265

The campaign life of blast furnace (BF) hearths has become the limiting factor for safety and high efficiency production of modern BFs. However, the earlywarning mechanism of hearth security has not been clear. In this article, based on heat transfer calculations, heat flux and erosion monitoring, the features of heat flux and erosion were analyzed and compared among different types of hearths. The primary detecting elements, mathematical models, evaluating standards, and warning methods were discussed. A novel earlywarning mechanism with the three-level quantificational standards was proposed for BF hearth security.

Today, Japanese society is well aware of the prediction of the Tokai earthquake. It is estimated by the Tokyo earthquake. It is estimated by the Tokyo muncipal government that this predicted earthquake could kill 30,000 people. (this estimate is viewed by many as conservative; other Japanese government agencies have made estimates but they have not been published.) Reduction in the number deaths from 120,000 to 30,000 between the Kanto earthquake and the predicted Tokai earthquake is due in large part to the reduction in the proportion of wooden construction (houses).

This paper chooses the agricultural listed companies as the research object, compares the financial situation of the enterprise and the theory of financial earlywarning, combines the financial status of the agricultural listed companies, selects the relevant cash flow indicators, discusses the application of the Logistic financial earlywarning model in the agricultural listed companies, Agricultural enterprises get better development. Research on financial earlywarning of agricultural listed companies will help the agricultural listed companies to predict the financial crisis. Financial earlywarning model is simple to establish, operational and strong, the use of financial earlywarning model, to help enterprises in the financial crisis before taking rapid and effective measures, which can avoid losses. Help enterprises to discover signs of deterioration of the financial situation in time to maintain the sustainable development of agricultural enterprises. In addition, through the financial earlywarning model, investors can correctly identify the financial situation of agricultural enterprises, and can evaluate the financial situation of agricultural enterprises and to help investors to invest in scientific and rational, beneficial to investors to analyze the safety of investment. But also help the relevant regulatory agencies to effectively monitor the market and promote the healthy and stable development of the market.

In complex systems, a critical transition is a shift in a system’s dynamical regime from its current state to a strongly contrasting state as external conditions move beyond a tipping point. These transitions are often preceded by characteristic earlywarning signals such as increased system variability. However, earlywarning signals in complex, coupled human–environment systems (HESs) remain little studied. Here, we compare critical transitions and their earlywarning signals in a coupled HES model to an equivalent environment model uncoupled from the human system. We parameterize the HES model, using social and ecological data from old-growth forests in Oregon. We find that the coupled HES exhibits a richer variety of dynamics and regime shifts than the uncoupled environment system. Moreover, the earlywarning signals in the coupled HES can be ambiguous, heralding either an era of ecosystem conservationism or collapse of both forest ecosystems and conservationism. The presence of human feedback in the coupled HES can also mitigate the earlywarning signal, making it more difficult to detect the oncoming regime shift. We furthermore show how the coupled HES can be “doomed to criticality”: Strategic human interactions cause the system to remain perpetually in the vicinity of a collapse threshold, as humans become complacent when the resource seems protected but respond rapidly when it is under immediate threat. We conclude that the opportunities, benefits, and challenges of modeling regime shifts and earlywarning signals in coupled HESs merit further research. PMID:27815533

A number of destructive meteotsunamis - atmospherically-driven long ocean waves in a tsunami frequency band - occurred during the last decade through the world oceans. Owing to significant damage caused by these meteotsunamis, several scientific groups (occasionally in collaboration with public offices) have started developing meteotsunami warning systems. Creation of one such system has been initialized in the late 2015 within the MESSI (Meteotsunamis, destructive long ocean waves in the tsunami frequency band: from observations and simulations towards a warning system) project. Main goal of this project is to build a prototype of a meteotsunami warning system for the eastern Adriatic coast. The system will be based on real-time measurements, operational atmosphere and ocean modeling and real time decision-making process. Envisioned MESSI meteotsunami warning system consists of three modules: (1) synoptic warning module, which will use established correlation between forecasted synoptic fields and high-frequency sea level oscillations to provide qualitative meteotsunami forecasts for up to a week in advance, (2) probabilistic premodeling prediction module, which will use operational WRF-ROMS-ADCIRC modeling system and compare the forecast with an atlas of presimulations to get the probabilistic meteotsunami forecast for up to three days in advance, and (3) real-time module, which is based on real time tracking of properties of air pressure disturbance (amplitude, speed, direction, period, ...) and their real-time comparison with the atlas of meteotsunami simulations. System will be tested on recent meteotsunami events which were recorded in the MESSI area shortly after the operational meteotsunami network installation. Albeit complex, such a multilevel warning system has a potential to be adapted to most meteotsunami hot spots, simply by tuning the system parameters to the available atmospheric and ocean data.

Ecological security earlywarning, as an important content of ecological security research, is of indicating significance in maintaining regional ecological security. Based on driving force, pressure, state, impact and response (D-P-S-I-R) framework model, this paper took Zhoushan Islands in Zhejiang Province as an example to construct the ecological security earlywarning index system, test degrees of ecological security earlywarning of Zhoushan Islands from 2000 to 2012 by using the method of variable weight model, and forecast ecological security state of 2013-2018 by Markov prediction method. The results showed that the variable weight model could meet the study needs of ecological security earlywarning of Zhoushan Islands. There was a fluctuant rising ecological security earlywarning index from 0.286 to 0.484 in Zhoushan Islands between year 2000 and 2012, in which the security grade turned from "serious alert" into " medium alert" and the indicator light turned from "orange" to "yellow". The degree of ecological security warning was "medium alert" with the light of "yellow" for Zhoushan Islands from 2013 to 2018. These findings could provide a reference for ecological security maintenance of Zhoushan Islands.

This book describes the interdisciplinary work of USAID's Famine EarlyWarning System Network (FEWS NET) and its influence on how food security crises are identified, documented and the kind of responses that result. The book describes FEWS NET's systems and methods for using satellite remote sensing to identify and describe how biophysical hazards impact the lives and livelihoods of the population where they occur. It presents several illustrative case studies that will demonstrate the integration of both physical and social science disciplines in its work. FEWS NET s operational needs have driven science in biophysical remote sensing applications through its collaboration with the US Geological Survey, the National Aeronautics and Space Administration, National Oceanographic and Atmospheric Administration, and US Department of Agriculture, as well as methodologies in the social science domain through its support of the US Agency for International Development, UNWorld Food Program and numerous international non-governmental organizations such as Save the Children, Oxfam and others. Because FEWS NET is an organization that must provide a global picture of food insecurity to decision makers, the information it relies on are by necessity observable and able to be documented. Thus many aspects of traditional livelihood analysis, for example, cannot be used by FEWS NET as they rely upon relationships, and ways of expressing power and knowledge at the local scale that cannot be easily scaled up to express variations in access to food at a community level. The book focuses on the ways that remote sensing information is transformed into an understanding of the actions that must be taken in order to ensure that lives and livelihoods are protected, including describing the remote sensing observations and models needed to identify hazards and the information gathering requirements and analytical frameworks needed to understand their impact. Its focus is primarily analysis

Usually, tsunami earlywarning and mitigation systems (TWS or TEWS) are based on several software components deployed in a client-server based infrastructure. The vast majority of systems importantly include desktop-based clients with a graphical user interface (GUI) for the operators in earlywarning centers. However, in times of cloud computing and ubiquitous computing the use of concepts and paradigms, introduced by continuously evolving approaches in information and communications technology (ICT), have to be considered even for earlywarning systems (EWS). Based on the experiences and the knowledge gained in three research projects - 'German Indonesian Tsunami EarlyWarning System' (GITEWS), 'Distant EarlyWarning System' (DEWS), and 'Collaborative, Complex, and Critical Decision-Support in Evolving Crises' (TRIDEC) - new technologies are exploited to implement a cloud-based and web-based prototype to open up new prospects for EWS. This prototype, named 'TRIDEC Cloud', merges several complementary external and in-house cloud-based services into one platform for automated background computation with graphics processing units (GPU), for web-mapping of hazard specific geospatial data, and for serving relevant functionality to handle, share, and communicate threat specific information in a collaborative and distributed environment. The prototype in its current version addresses tsunami earlywarning and mitigation. The integration of GPU accelerated tsunami simulation computations have been an integral part of this prototype to foster earlywarning with on-demand tsunami predictions based on actual source parameters. However, the platform is meant for researchers around the world to make use of the cloud-based GPU computation to analyze other types of geohazards and natural hazards and react upon the computed situation picture with a web-based GUI in a web browser at remote sites. The current website is an early alpha version for demonstration purposes to give the

Flood earlywarning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood earlywarning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood earlywarning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to earlywarning systems.

In Western Norway, landslides and debris flows are commonly initiated by short-term orographic rainfall or intensity peaks during a prolonged rainfall event. In recent years, the flood warning service in Norway has evolved from being solely a flood forecasting service to also integrating landslides into its earlywarning systems. As both floods and landslides are closely related to the same hydrometeorological processes, particularly in small catchments, there is a natural synergy between monitoring flood and landslide risk. The Norwegian Flood and Landslide Hazard Forecasting and Warning Service issues regional landslide hazard warnings based on hydrological models, threshold values, observations and weather forecasts. Intense rainfall events and/or orographic precipitation that, under certain topographic conditions, significantly increase the risk of debris avalanches and debris floods are lately receiving more research focus from the Norwegian warning service. Orographic precipitation is a common feature in W-Norway, when moist and relatively mild air arrives from the Atlantic. Steep mountain slopes covered by glacial till makes the region prone to landslides, as well as flooding. The operational earlywarning system in Norway requires constant improvement, especially with the enhanced number of intense rainfall events that occur in a warming climate. Here, we examine different cases of intense rainfall events which have lead to landslides and debris flows, as well as increased runoff in fast responding small catchments. The main objective is to increase the understanding of the hydrometeorological conditions related to these events, in order to make priorities for the future development of the warning service.

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and earlywarning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide earlywarning and suggests that a warning be issued at switch of wave velocity decrease rate.

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and earlywarning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide earlywarning and suggests that a warning be issued at switch of wave velocity decrease rate. PMID:29584699

In this era of rapid social and technological change leading to interesting life complexity and environmental displacement, both positive and negative effects among ecosystems call for a balance in which there are impacts by climate changes. Earlywarning systems for climate change impacts are necessary in order to allow society as a whole to properly and usefully assimilate the masses of new information and knowledge. Therefore, our research addresses to build up a sustainable earlywarning mechanism. The main goal is to mitigate the cumulative impacts on the environment of climate change and enhance adaptive capacities. An effective earlywarning system has been proven for protection. However, there is a problem that estimate future climate changes would be faced with high uncertainty. In general, take estimations for climate change impacts would use the data from General Circulation Models and take the analysis as the Intergovernmental Panel on Climate Change declared. We follow the course of the method for analyzing climate change impacts and attempt to accomplish the sustainable earlywarning system for water quality management. Climate changes impact not only on individual situation but on short-term variation and long-term gradually changes. This kind characteristic should adopt the suitable warning system for long-term formulation and short- term operation. To continue the on-going research of the long-term earlywarning system for climate change impacts on water quality management, the short-term earlywarning system is established by using local observation data for reappraising the warning issue. The combination of long-term and short-term system can provide more circumstantial details. In Taiwan, a number of studies have revealed that climate change impacts on water quality, especially in arid period, the concentration of biological oxygen demand may turn into worse. Rapid population growth would also inflict injury on its assimilative capacity to

Malaria early detection and earlywarning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (earlywarning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

In the current hazard research people-centred warning becomes more and more important, because different types of organizations and groups have to be involved in the warning process. This fact has to be taken into account when developing earlywarning systems. The effectiveness of earlywarning depends not only on technical capabilities but also on the preparedness of decision makers and their immediate response on how to act in case of emergency. Hence earlywarning systems have to be regarded in the context of an integrated and holistic risk management. Disaster Risk Reduction (DRR) measures include people-centred, timely and understandable warning. Further responsible authorities have to be identified in advance and standards for risk communication have to be established. Up to now, hazard and risk assessment for geohazards focuses on the development of inventory, susceptibility, hazard and risk maps. But often, especially in Europe, there are no institutional structures for managing geohazards and in addition there is a lack of an authority that is legally obliged to alarm on landslides at national or regional level. One of the main characteristics within the warning process for natural hazards e.g. in Germany is the split of responsibility between scientific authorities (wissenschaftliche Fachbehörde) and enforcement authorities (Vollzugsbehörde). The scientific authority provides the experts who define the methods and measures for monitoring and evaluate the hazard level. The main focus is the acquisition and evaluation of data and subsequently the distribution of information. The enforcement authority issues official warnings about dangerous natural phenomena. Hence the information chain in the context of earlywarning ranges over two different institutions, the forecast service and the warning service. But there doesn't exist a framework for warning processes in terms of landslides as yet. The concept for managing natural disasters is often reduced to

The paper proposes a risk-based earlywarning considering characteristics of fracture-karst aquifer in North China and applied it in a super-large fracture-karst water source. Groundwater vulnerability, types of land use, water abundance, transmissivity and spatial temporal variation of groundwater quality were chosen as indexes of the method. Weights of factors were obtained by using AHP method based on relative importance of factors, maps of factors were zoned by GIS, earlywarning map was conducted based on extension theory with the help of GIS, ENVI+IDL. The earlywarning map fused five factors very well, serious and tremendous warning areas are mainly located in northwest and east with high or relatively high transmissivity and groundwater pollutant loading, and obviously deteriorated or deteriorated trend of petroleum. The earlywarning map warns people where more attention should be paid, and the paper guides decision making to take appropriate protection actions in different warning levels areas.

Warning indicators of the dam body's temperature are required for the real-time monitoring of the service conditions of concrete dams to ensure safety and normal operations. Warnings theories are traditionally targeted at a single point which have limitations, and the scientific warning theories on global behavior of the temperature field are non-existent. In this paper, first, in 3D space, the behavior of temperature field has regional dissimilarity. Through the Ward spatial clustering method, the temperature field was divided into regions. Second, the degree of order and degree of disorder of the temperature monitoring points were defined by the probability method. Third, the weight values of monitoring points of each regions were explored via projection pursuit. Forth, a temperature entropy expression that can describe degree of order of the spatial temperature field in concrete dams was established. Fifth, the early-warning index of temperature entropy was set up according to the calculated sequential value of temperature entropy. Finally, project cases verified the feasibility of the proposed theories. The early-warning index of temperature entropy is conducive to the improvement of early-warning ability and safety management levels during the operation of high concrete dams.

This discussion article focuses on the literature surrounding earlywarning scoring systems and their use in primary care, specifically within district nursing. Patient deterioration is a global concern, associated with high mortality rates and avoidable deaths. Early recognition and response by nursing and other health care staff has been attributed to earlywarning scoring systems (EWSS) and tools. However, the use of equivalent tools in the community appears to be lacking. This review concludes that there is no consensus over the use of EWSS in district nursing and culture of practice is varied, rather than standardised.

The Massachusetts Department of Elementary and Secondary Education first released the EarlyWarning Indicator System (EWIS) data for grades 1-12 in the 2012-13 school year. The Department created the EWIS in direct response to educators' requests for early indicator data across multiple grade levels. The EWIS is a "tool to systematically…

This article describes the development of earlywarning indicators for high school and beyond in the Milwaukee Public Schools (MPS) by the Value-Added Research Center (VARC) at the University of Wisconsin-Madison, working in conjunction with staff from the Division of Research and Evaluation at MPS. Our work in MPS builds on prior early warning…

This paper discusses some methodological questions on understanding disasters. Destructive earthquakes continue to claim thousands of lives. Tsunamis may be caused by recoil of the upper plate. Darwin's twin-epicenter hypothesis is applied to a theory of tsunamis. The ergodicity hypothesis may help estimating the return periods of extremely rare events. A social science outline on the causation of the Tôhoku nuclear disaster is provided.

This paper discusses some methodological questions on understanding disasters. Destructive earthquakes continue to claim thousands of lives. Tsunamis may be caused by recoil of the upper plate. Darwin's twin-epicenter hypothesis is applied to a theory of tsunamis. The ergodicity hypothesis may help to estimate the return periods of extremely rare events. A social science outline on the causation of the Tôhoku nuclear disaster is provided.

Occurrence of fast landslides has become more and more dangerous during the last decades, due to the increased density of settlements, industrial plants and infrastructures. Such problem is particularly worrying in Campania (Southern Italy), where the fast population growth led a diffuse building activity without planning: indeed, recent flowslides caused hundreds of victims and heavy damages to buildings, roads and other infrastructures. Large mountainous areas in Campania are mantled by loose pyroclastic granular soils up to a depth of a few meters from top soil surface. These soils have usually a grain size that falls in the domain of silty sands, including pumice interbeds (gravelly sands), with saturated hydraulic conductivities up to the order of 10-1 cm/min. Such deposits often cover steep slopes, which stability is guaranteed by the apparent cohesion due to suction under unsaturated conditions, that are the most common conditions for these slopes [Olivares and Picarelli, 2001]. Whereas rainfall infiltration causes soil to approach saturation, suction vanishes and slope failure may occur. Besides soil physical properties, landslide triggering is influenced by several factors, such as rainfall intensity, soil initial moisture and suction, slope inclination, boundary conditions. Whereas slope failure occurs with soil close to being saturated, landslide may develop in form of fast and destructive flowslide. Calibration of reliable mathematical models of such a complex phenomenon requires availability of experimental observations of the major variables of interest, such as soil moisture and suction, soil deformation and displacements, pore water pressure, during the entire process of infiltration until slope failure. Due to the sudden trigger and extremely rapid propagation of such type of landslides, such data sets are rarely available for natural slopes where flowslides occurred. As a consequence landslide risk assessment and earlywarning in Campania rely on

China’s industry boom has not only brought unprecedented prosperity, but also caused the gradual depletion of various resources and the worsening of the natural environment. Experts admit that China is facing serious environmental problem, but they believe that they can seek a new path to overcome it through joint efforts. Earlywarning information release and clean production are the important concepts in addressing the imminent crisis. Earlywarning information release system can monitor and forecast the risk that affects the clean production. The author drawn the experiences and lessons from developed countries, combined with China’s reality, put forward countermeasures and suggestions about constructing earlywarning information release system in process of Chinese town-scaled enterprises clean production.

With increased changes in land cover and global climate, early detection and warning of dust storms in conjunction with effective and widespread information broadcasts will be essential to the prevention and mitigation of future risks and impacts. Human activities, seasonal variations and long-term climatic patterns influence dust storms. More research is needed to analyse these factors of dust mobilisation to create more certainty for the fate of vulnerable populations and ecosystems in the future. Earlywarning and communication systems, when in place and effectively implemented, can offer some relief to these vulnerable areas. As an issue that affects many regions of the world, there is a profound need to understand the potential changes and ultimately create better earlywarning systems for dust storms.

The earlywarning of supplier performance risk is still in the initial stage interiorly, and research on the earlywarning mechanism to identify, analyze and prevent the performance risk is few. In this paper, a new method aiming at marerial supplier performance risk in power industry is proposed, firstly, establishing a set of risk earlywarning indexes, Then use the ECM method to classify the indexes to form different risk grades. Then, improving Crock Ford risk quantization model by considering three indicators, including the stability of power system, economic losses and successful bid ratio to form the predictive risk grade, and ultimately using short board effect principle to form the ultimate risk grade to truly reflect the supplier performance risk. Finally, making empirical analysis on supplier performance and putting forward the counter measures and prevention strategies for different risks.

An event-driven, urban, drinking water quality earlywarning and control system (DEWS) is proposed to cope with China's urgent need for protecting its urban drinking water. The DEWS has a web service structure and provides users with water quality monitoring functions, water quality earlywarning functions, and water quality accident decision-making functions. The DEWS functionality is guided by the principles of control theory and risk assessment as applied to the feedback control of urban water supply systems. The DEWS has been deployed in several large Chinese cities and found to perform well insofar as water quality earlywarning and emergency decision-making is concerned. This paper describes a DEWS for urban water quality protection that has been developed in China.

The aim of this paper is developing and testing of landslide earlywarning system. The earlywarning system uses accelerometersas ground movement and tilt-sensing device and a water flow sensor. A microcentroller is used to process the input signal and activate the alarm. An LCD is used to display the acceleration in x,y and z axis. When the soil moved or shifted and rainfall reached 100 mm/day, the alarm rang and signal were sentto the monitoring center via a telemetry system.Data logging information and GIS spatial data can be monitored remotely as tables and graphics as well as in the form of geographical map with the help of web-GIS interface. The system were tested at Kampung Gerendong, Desa Putat Nutug, Kecamatan Ciseeng, Kabupaten Bogor. This area has 3.15 cumulative score, which mean vulnerable to landslide. The results show that the earlywarning system worked as planned.

Glacial lake outburst floods (GLOFs) are serious disasters in glacial areas. At present, glaciers are retreating while glacial lake area and the outburst risk increases due to the global warming. Therefore, the research of earlywarning method of GLOFs is important to prevent and reduce the disasters. This paper provides an earlywarning method using the temperature and rainfall as indices. The daily growth rate of positive antecedent accumulative temperature and the antecedent thirty days accumulative precipitation are calculated for 21 events of GLOF before 2010, based on data from the 21 meteorological stations nearby. The result shows that all the events are above the curve, TV = -0.0193RDC + 3.0018, which can be taken as the earlywarning threshold curve. This has been verified by the GLOF events in the Ranzeaco glacial lake on 2013-07-05.

to evaluate the accuracy of the version of the Brighton Pediatric EarlyWarning Score translated and adapted for the Brazilian context, in the recognition of clinical deterioration. a diagnostic test study to measure the accuracy of the Brighton Pediatric EarlyWarning Score for the Brazilian context, in relation to a reference standard. The sample consisted of 271 children, aged 0 to 10 years, blindly evaluated by a nurse and a physician, specialists in pediatrics, with interval of 5 to 10 minutes between the evaluations, for the application of the Brighton Pediatric EarlyWarning Score for the Brazilian context and of the reference standard. The data were processed and analyzed using the Statistical Package for the Social Sciences and VassarStats.net programs. The performance of the Brighton Pediatric EarlyWarning Score for the Brazilian context was evaluated through the indicators of sensitivity, specificity, predictive values, area under the ROC curve, likelihood ratios and post-test probability. the Brighton Pediatric EarlyWarning Score for the Brazilian context showed sensitivity of 73.9%, specificity of 95.5%, positive predictive value of 73.3%, negative predictive value of 94.7%, area under Receiver Operating Characteristic Curve of 91.9% and the positive post-test probability was 80%. the Brighton Pediatric EarlyWarning Score for the Brazilian context, presented good performance, considered valid for the recognition of clinical deterioration warning signs of the children studied. avaliar a acurácia da versão traduzida e adaptada do Brighton Paediatric EarlyWarning Score para o contexto brasileiro, no reconhecimento da deterioração clínica. estudo de teste diagnóstico para medir a acurácia do Brighton Paediatric EarlyWarning Score, para o contexto brasileiro, em relação a um padrão de referência. A amostra foi composta por 271 crianças de 0 a 10 anos, avaliadas de forma cega por uma enfermeira e um médico, especialistas em pediatria, com

Groundwater pollution usually is complex and concealed, remediation of which is difficult, high cost, time-consuming, and ineffective. An earlywarning system for groundwater pollution is needed that detects groundwater quality problems and gets the information necessary to make sound decisions before massive groundwater quality degradation occurs. Groundwater pollution earlywarning were performed by considering comprehensively the current groundwater quality, groundwater quality varying trend and groundwater pollution risk . The map of the basic quality of the groundwater was obtained by fuzzy comprehensive evaluation or BP neural network evaluation. Based on multi-annual groundwater monitoring datasets, Water quality state in sometime of the future was forecasted using time-sequenced analyzing methods. Water quality varying trend was analyzed by Spearman's rank correlative coefficient.The relative risk map of groundwater pollution was estimated through a procedure that identifies, cell by cell,the values of three factors, that is inherent vulnerability, load risk of pollution source and contamination hazard. DRASTIC method was used to assess inherent vulnerability of aquifer. Load risk of pollution source was analyzed based on the potential of contamination and pollution degree. Assessment index of load risk of pollution source which involves the variety of pollution source, quantity of contaminants, releasing potential of pollutants, and distance were determined. The load risks of all sources considered by GIS overlay technology. Earlywarning model of groundwater pollution combined with ComGIS technology organically, the regional groundwater pollution early-warning information system was developed, and applied it into Qiqiha'er groundwater earlywarning. It can be used to evaluate current water quality, to forecast water quality changing trend, and to analyze space-time influencing range of groundwater quality by natural process and human activities. Keywords

A drought prone region such as the Great Plains of the United States (US GP) requires credible and actionable drought earlywarning. Such information cannot simply be extracted from available climate forecasts because of their large uncertainties at regional scales, and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA North American Multi-Model Ensemble experiment (NMME) are much more reliable for winter and spring than for the summer season for the US GP. To mitigate the weaknesses of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies, as the scientific basis for a statistical drought earlywarning system. This system uses percentile soil moisture anomalies in spring as a key input to provide a probabilistic summer drought earlywarning. The latter outperforms the dynamic prediction over the US Southern Plains and has been used by the Texas state water agency to support state drought preparedness. A main source of uncertainty for this drought earlywarning system is the soil moisture input obtained from the NOAA Climate Forecasting System (CFS). We are testing use of the beta version of NASA Soil Moisture Active Passive (SMAP) soil moisture data, along with the Soil Moisture and Ocean Salinity (SMOS), and the long-term Essential Climate Variable Soil Moisture (ECV-SM) soil moisture data, to reduce this uncertainty. Preliminary results based on ECV-SM suggests satellite based soil moisture data could improve earlywarning of rainfall anomalies over the western US GP with less dense vegetation. The skill degrades over the eastern US GP where denser vegetation is found. We evaluate our SMAP-based drought earlywarning for 2015 summer against observations.

This is the PDF of a presentation for a webinar given by Los Alamos National Laboratory (LANL) on the earlywarning or detection of global re-emerging infectious disease (RED). First, there is an overview of LANL biosurveillance tools. Then, information is given about RED Alert. Next, a demonstration is given of a component prototype. RED Alert is an analysis tool that can provide earlywarning or detection of the re-emergence of an infectious disease at the global level, but through a local lens.

The Norwegian Water Resources and Energy Directorate (NVE) runs the national earlywarning systems (EWS) for flooding and shallow landslides in Norway. The two EWSs have been operational since the late 1980s and 2013 respectively, and are based on weather forecasts, various hydro-meteorological prognosis and expert evaluation. Daily warning levels and related information to the public is prepared and presented through custom build internet platforms. In natural hazards sciences, the risk of a specific threat is defined as the product of hazard and consequence. In this context an EWS is intended to work as a mitigation measure in lowering the consequence and thus the risk of the threat. One of several factors determining the quality of such an EWS, is how warnings are communicated to the public. In contrary to what is common practice in some other countries, experts working with EWS in Norway cannot be held personally responsible for consequences of warnings being issued or not. However, the communication of warnings for flooding and landslides at NVE still implies many considerations of geoethical kind. Which are the consequences today for the forecasters when erroneous warning messages are sent because based on a poorly documented analysis? What is for example the most responsible way to describe uncertainties in warnings issued? What is the optimal compromise between avoiding false alarms and not sending out a specific warning? Is it responsible to rely on a "gut feeling"? Some authorities complain in receiving warning messages too often. Is it responsible to begin notifying these, only in cases of "high hazard level" and no longer in cases of "moderate hazard level"? Is it acceptable to issue general warnings for large geographical areas without being able to pinpoint the treat on local scale? What responsibility lies within the EWS in recommending evacuation or other practical measures to local authorities? By presenting how earlywarnings of flooding and

Most people are aware that outdoor air pollution can damage their health, but many do not know that indoor air pollution can also exhibit significant negative health effects. Fungi parasitizing in air conditioning and ventilation systems can be one of indoor air pollution sources. Aflatoxin produced by Aspergillus flavus (A. flavus) became a central focus of indoor air pollution, especially in farmer markets. Therefore we developed an earlywarning system, Health Risk Assessment System, to estimate the growth rate of A. flavus, predict the amount of aflatoxin and provide earlywarning information. Firstly, the growth of A. flavus and the production of aflatoxin under different conditions were widely obtained through a comprehensive literature review. Secondly, three mathematical models were established to predict the A. flavus colony growth rate, lag phase duration and aflatoxin content, as functions of temperature and water activity based on present studies. Finally, all the results were evaluated by the user-supplied data using PHP programming language. We utilized the web page to show the results and display warning information. The JpGraph library was used to create a dynamic line chart, refreshing the warning information dynamically in real-time. The HARS provides accurate information for earlywarning purposes to let us take timely steps to protect ourselves.

The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic earlywarning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that earlywarning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached. PMID:25482065

The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic earlywarning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that earlywarning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached.

The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic earlywarning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that earlywarning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached.

... Search Term(s): Main Content Home Be Informed EarthquakesEarthquakes An earthquake is the sudden, rapid shaking of the earth, ... by the breaking and shifting of underground rock. Earthquakes can cause buildings to collapse and cause heavy ...

U.S. forests occupy approx 751 million acres (approx 1/3 of total land). Several abiotic and biotic damage agents disturb, damage, kill, and/or threaten these forests. Regionally extensive forest disturbances can also threaten human life and property, bio-diversity and water supplies. timely regional forest disturbance monitoring products are needed to aid forest health management work at finer scales. daily MODIS data provide a means to monitor regional forest disturbances on a weekly basis, leveraging vegetation phenology. In response, the USFS and NASA began collaborating in 2006 to develop a Near Real Time (NRT) forest monitoring capability, based on MODIS NDVI data, as part of a national forest threat EarlyWarning System (EWS).

Landslide earlywarning systems (LEWSs) reduce landslide risk by disseminating timely and meaningful warnings when the level of risk is judged intolerably high. Two categories of LEWSs, can be defined on the basis of their scale of analysis: "local" systems and "regional" systems. LEWSs at regional scale (ReLEWSs) are used to assess the probability of occurrence of landslides over appropriately-defined homogeneous warning zones of relevant extension, typically through the prediction and monitoring of meteorological variables, in order to give generalized warnings to the public. Despite many studies on ReLEWSs, no standard requirements exist for assessing their performance. Empirical evaluations are often carried out by simply analysing the time frames during which significant high-consequence landslides occurred in the test area. Alternatively, the performance evaluation is based on 2x2 contingency tables computed for the joint frequency distribution of landslides and alerts, both considered as dichotomous variables. In all these cases, model performance is assessed neglecting some important aspects which are peculiar to ReLEWSs, among which: the possible occurrence of multiple landslides in the warning zone; the duration of the warnings in relation to the time of occurrence of the landslides; the level of the warning issued in relation to the landslide spatial density in the warning zone; the relative importance system managers attribute to different types of errors. An original approach, called EDuMaP method, is proposed to assess the performance of landslide earlywarning models operating at regional scale. The method is composed by three main phases: Events analysis, Duration Matrix, Performance analysis. The events analysis phase focuses on the definition of landslide (LEs) and warning events (WEs), which are derived from available landslides and warnings databases according to their spatial and temporal characteristics by means of ten input parameters. The

Majalaya, a small city to the south-east of Bandung, was hit by flood almost every year. From January to June 2016, up to 5 severe floods and 4 moderate floods have hit this city. Although it usually not last for long, but the flood stream could be very rapid, thus have a high potential to bring damage to the city. Starting from 2012, ITB through Weather and Climate Prediction Laboratory (WCPL) has support Garda Caah (flood watcher society in Majalaya) with weather prediction system. In the late 2015, ITB also enhancing Garda Caah observation system by installing several Automatic Weather Station (AWS) and Automatic Water Level Recorder (AWLR) throughout Majalaya upstream area. The instruments itself was supported by a re-insurance company MAIPARK and some was built in house by WCPL. The collaboration between ITB, Garda Caah, and Majalaya citizens has been proved to be mutually beneficial. Garda Caah could get more accurate and faster observation and enhanced knowledge, thus could provide a better flood warning for Majalaya citizens. On the other hand, ITB could get data from observation network, with more efficient way to maintain observation instruments as it done by Garda Caah and other Majalaya citizens.

The EarlyWarning System (EWS) Tool v2.0 is a Microsoft Excel-based tool developed by the National High School Center at the American Institutes for Research in collaboration with Matrix Knowledge Group. The tool enables schools, districts, and states to identify students who may be at risk of dropping out of high school and to monitor these…

Imagine a national system with the ability to quickly identify forested areas under attack from insects or disease. Such an earlywarning system might minimize surprises such as the explosion of caterpillars referred to in the quotation above. Moderate resolution (ca. 500m) remote sensing repeated at frequent (ca. weekly) intervals could power such a monitoring system...

The National Integrated Drought Information System (NIDIS) and the Carolinas IntegratedÂ Sciences and Assessments (CISA), a National Oceanic and Atmospheric Administration (NOAA)-Â funded Regional Integrated Sciences and Assessments (RISA) program, are partnering to developÂ and support a Carolinas Drought EarlyWarning System pilot program. Research and projectsÂ focus on...

This paper reports on a project in which students designed, constructed and tested a model of an existing earlywarning system with simulation of debris flow in a context of a landslide. Students also assessed rural community members' knowledge of this system and subsequently taught them to estimate the time needed for evacuation of the community…

The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout EarlyWarning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these earlywarning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of earlywarning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of earlywarning signals across multiple environments as "indicators for loss of resilience." We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability-resilience relation needs to be better understood for the application of earlywarning signals in different scenarios.

This paper focuses on the use of community-based earlywarning systems for flood resilience in Nepal. The first part of the work outlines the evolution and current status of these community-based systems, highlighting the limited lead times currently available for earlywarning. The second part of the paper focuses on the development of a robust operational flood forecasting methodology for use by the Nepal Department of Hydrology and Meteorology (DHM) to enhance earlywarning lead times. The methodology uses data-based physically interpretable time series models and data assimilation to generate probabilistic forecasts, which are presented in a simple visual tool. The approach is designed to work in situations of limited data availability with an emphasis on sustainability and appropriate technology. The successful application of the forecast methodology to the flood-prone Karnali River basin in western Nepal is outlined, increasing lead times from 2-3 to 7-8 h. The challenges faced in communicating probabilistic forecasts to the last mile of the existing community-based earlywarning systems across Nepal is discussed. The paper concludes with an assessment of the applicability of this approach in basins and countries beyond Karnali and Nepal and an overview of key lessons learnt from this initiative.

Increasing external pressures from human activities and climate change can lead to desertification, affecting the livelihood of more than 25% of the worldâs population. Thus, determining proximity to transition to desertification is particularly central for arid regions before they may convert into deserts, and recent research has focused on devising earlywarning...

Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient’s cardiac activities for earlywarning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An earlywarning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the earlywarning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed earlywarning system. PMID:28353681

Although high school graduation rates continue to rise in the United States, reaching 81 percent in the 2012-2013 school year (U.S. Department of Education, 2015), dropout remains a pervasive issue for education systems across the nation. In recent years, EarlyWarning Systems (EWS), which utilize administrative data to identify students at risk…

The Homeland Protection Act of 2002 specifically calls for the investigation and use of EarlyWarning Systems (EWS) for water security reasons. The EWS is a screening tool for detecting changes in source water and distribution system water quality. A suite of time-relevant biol...

The Homeland Protection Act of 2002 specifically calls for the investigation and use of EarlyWarning Systems (EWS) for water security reasons. The EWS is a screening tool for detecting changes in source water and distribution system water quality. A suite of time-relevant biol...

We investigate the potential of geospatiotemporal data mining of multi-year land surface phenology data (250 m Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study) for the conterminous United States as part of an earlywarning system to identify threats to forest ecosystems. Cluster...

Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient's cardiac activities for earlywarning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An earlywarning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the earlywarning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed earlywarning system.

The quality and the effectiveness of the 1992 New Jersey Grade 8 EarlyWarning Test (NJEWT) are assessed. Standardized tests possess clear advantages for educators, especially in the case of administration and scoring, but there are clear disadvantages as well, including the possibility of bias. Four criteria are applied to the NJEWT: adequacy,…

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these earlywarning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of earlywarning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of earlywarning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of earlywarning signals in different scenarios. PMID:26216946

To stem the tide of students dropping out, many schools and districts are turning to earlywarning systems (EWS) that signal whether a student is at risk of not graduating from high school. While some research exists about establishing these systems, there is little information about the actual implementation strategies that are being used across…

Landslide earlywarning systems at regional scale are used to warn authorities, civil protection personnel and the population about the occurrence of rainfall-induced landslides over wide areas, typically through the prediction and measurement of meteorological variables. A warning model for these systems must include a regional correlation law and a decision algorithm. A regional correlation law can be defined as a functional relationship between rainfall and landslides; it is typically based on thresholds of rainfall indicators (e.g., cumulated rainfall, rainfall duration) related to different exceedance probabilities of landslide occurrence. A decision algorithm can be defined as a set of assumptions and procedures linking rainfall thresholds to warning levels. The design and the employment of an operational and reliable earlywarning system for rainfall-induced landslides at regional scale depend on the identification of a reliable correlation law as well as on the definition of a suitable decision algorithm. Herein, a five-step process chain addressing both issues and based on rainfall thresholds is proposed; the procedure is tested in a landslide-prone area of the Campania region in southern Italy. To this purpose, a database of 96 shallow landslides triggered by rainfall in the period 2003-2010 and rainfall data gathered from 58 rain gauges are used. First, a set of rainfall thresholds are defined applying a frequentist method to reconstructed rainfall conditions triggering landslides in the test area. In the second step, several thresholds at different exceedance probabilities are evaluated, and different percentile combinations are selected for the activation of three warning levels. Subsequently, within steps three and four, the issuing of warning levels is based on the comparison, over time and for each combination, between the measured rainfall and the pre-defined warning level thresholds. Finally, the optimal percentile combination to be employed in

Disaster risk reduction has long been recognized for its role in mitigating the negative environmental, social and economic impacts of natural hazards. Flood EarlyWarning System is a disaster risk reduction measure based on the capacities of institutions to observe and predict extreme hydro-meteorological events and to disseminate timely and meaningful warning information; it is furthermore based on the capacities of individuals, communities and organizations to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss. An operational definition of an EarlyWarning System has been suggested by ISDR - UN Office for DRR [15 January 2009]: "EWS is the set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss.". ISDR continues by commenting that a people-centered earlywarning system necessarily comprises four key elements: 1-knowledge of the risks; 2-monitoring, analysis and forecasting of the hazards; 3-communication or dissemination of alerts and warnings; and 4- local capabilities to respond to the warnings received." The technological platform DEWETRA supports the strengthening of the first three key elements of EWS suggested by ISDR definition, hence to improve the capacities to build real-time risk scenarios and to inform and warn the population in advance The technological platform DEWETRA has been implemented for the Republic of Serbia. DEWETRA is a real time-integrate system that supports decision makers for risk forecasting and monitoring and for distributing warnings to end-user and to the general public. The system is based on the rapid availability of different data that helps to establish up-to-date and reliable risk scenarios. The integration of all relevant data for risk management significantly

the ICG/NEAMTWS NEAMWave12 exercise for the Turkish and Portuguese tsunami exercise scenarios. Impressions gained with the standards compliant TRIDEC system during the exercise will be reported. The system version presented is based on event-driven architecture (EDA) and service-oriented architecture (SOA) concepts and is making use of relevant standards of the Open Geospatial Consortium (OGC), the World Wide Web Consortium (W3C) and the Organization for the Advancement of Structured Information Standards (OASIS). In this way the system continuously gathers, processes and displays events and data coming from open sensor platforms to enable operators to quickly decide whether an earlywarning is necessary and to send personalized warning messages to the authorities and the population at large through a wide range of communication channels. The system integrates OGC Sensor Web Enablement (SWE) compliant sensor systems for the rapid detection of hazardous events, like earthquakes, sea level anomalies, ocean floor occurrences, and ground displacements. Using OGC Web Map Service (WMS) and Web Feature Service (WFS) spatial data are utilized to depict the situation picture. The integration of a simulation system to identify affected areas is considered using the OGC Web Processing Service (WPS). Warning messages are compiled and transmitted in the OASIS Common Alerting Protocol (CAP) together with addressing information defined via the OASIS Emergency Data Exchange Language - Distribution Element (EDXL-DE). This demonstration is linked with the talk 'Experiences with TRIDEC's Crisis Management Demonstrator in the Turkish NEAMWave12 exercise tsunami scenario' (EGU2013-2833) given in the session "Architecture of Future Tsunami Warning Systems" (NH5.6).

Strong El Niño events have a marked impact on regional climate worldwide through their influence on large-scale atmospheric circulation. As a result, seasonal climate forecasts show greater skill during El Niño events, which provide communities, governments and humanitarian agencies greater ability to plan and prepare. The scientific community has advanced considerably in the quality and content of information provided about El Niño and its impacts. As a result, society has become better aware of and engaged with this information. This talk will present some details on how we navigate the fine line between expectations and probabilistic forecasts, and how this information was used during the 2015-16 El Niño event. Examples are drawn from the health sector and food security community. Specific attention will be given to the importance of problem-focus and data availability in the appropriate tailoring of climate information for EarlyWarning/Early Action.

Vegetative phenology is the study of plant development and changes with the seasons, such as the greening-up and browning-down of forests, and how these events are influenced by variations in climate. A National Phenology Data Set, based on Moderate Resolution Imaging Spectroradiometer satellite images covering 2002 through 2009, is now available from work by NASA, the US Forest Service, and Oak Ridge National Laboratory. This new data set provides an easily interpretable product useful for detecting changes to the landscape due to long-term factors such as climate change, as well as finding areas affected by short-term forest threats such as insects or disease. The EarlyWarning System (EWS) is a toolset being developed by the US Forest Service and the University of North Carolina-Asheville to support distribution and use of the National Phenology Data Set. The EarlyWarning System will help research scientists, US Forest Service personnel, forest and natural resources managers, decision makers, and the public in the use of phenology data to better understand unexpected change within our nation’s forests. These changes could have multiple natural sources such as insects, disease, or storm damage, or may be due to human-induced events, like thinning, harvest, forest conversion to agriculture, or residential and commercial use. The primary goal of the EarlyWarning System is to provide a seamless integration between monitoring, detection, earlywarning and prediction of these forest disturbances as observed through phenological data. The system consists of PC and web-based components that are structured to support four user stages of increasing knowledge and data sophistication. Building Literacy: This stage of the EarlyWarning System educates potential users about the system, why the system should be used, and the fundamentals about the data the system uses. The channels for this education include a website, interactive tutorials, pamphlets, and other technology

A preliminary investigation of the earlywarning of solar storms caused by Coronal Mass Ejection has been carried out. A long warning time could be obtained with a sailcraft synchronous with the Earth-Moon barycenter, and stationed well below the L1 point. In this paper, the theory of heliocentric synchronous sailcraft is set up, its perturbed orbit is analyzed, and a potential solution capable of providing an annual synchrony is carried out. A simple analysis of the response from a low-mass electrochromic actuator for the realization of station-keeping attitude maneuvers is put forwards, and an example of propellantless re-orientation maneuver is studied.

Earlywarning systems can provide critical information for operations managers, emergency planners, and the public to help reduce fatalities, injuries, and economic losses due to landsliding. For shallow, rainfall-triggered landslides earlywarning systems typically use empirical rainfall thresholds, whereas the actual triggering mechanism involves the non-linear hydrological processes of infiltration, evapotranspiration, and hillslope drainage that are more difficult to quantify. Because hydrologic monitoring has demonstrated that shallow landslides are often preceded by a rise in soil moisture and pore-water pressures, some researchers have developed earlywarning criteria that attempt to account for these antecedent wetness conditions through relatively simplistic storage metrics or soil-water balance modeling. Here we explore the potential for directly incorporating antecedent wetness into landslide earlywarning criteria using recent landslide inventories and in-situ hydrologic monitoring near Seattle, WA, and Portland, OR. We use continuous, near-real-time telemetered soil moisture and pore-water pressure data measured within a few landslide-prone hillslopes in combination with measured and forecasted rainfall totals to inform easy-to-interpret landslide initiation thresholds. Objective evaluation using somewhat limited landslide inventories suggests that our new thresholds based on subsurface hydrologic monitoring and rainfall data compare favorably to the capabilities of existing rainfall-only thresholds for the Seattle area, whereas there are no established rainfall thresholds for the Portland area. This preliminary investigation provides a proof-of-concept for the utility of developing landslide earlywarning criteria in two different geologic settings using real-time subsurface hydrologic measurements from in-situ instrumentation.

first case we will try at least to reproduce the observed signal, otherwise we will try to understand whether the non-tsunamigenic nature of the event is confirmed by the tsunami simulations. The second problem is more related to tsunami earlywarning issues, in particular with the performance of the Tsunami Decision Matrix for the Mediterranean, presently adopted for example by the candidate Tsunami Service Providers at NOA (Greece) and INGV (Italy). We will briefly discuss whether the present form of the matrix, which does not include any information on focal mechanism, is well suited to a peculiar event like the November 17 earthquake, which was of strike-slip nature and had a magnitude lying just at the border between two distinct classes of tsunami potential forecast. This study is funded in the frame of the EU Project called ASTARTE - "Assessment, STrategy And Risk Reduction for Tsunamis in Europe", Grant 603839, 7th FP (ENV.2013.6.4-3), and of the Italian Flagship Project RITMARE ("La Ricerca ITaliana per il MARE").

Satellites EarlyWarning System Series class SBIRS US Air Force must replace on GEO early series DSP Series. During 2014-2016 the authors received more than 30 light curves "DSP-18 and "Sbirs-Geo 2". The analysis of the behavior of these satellites in orbit by a coordinate and photometric data. It is shown that for the monitoring of the Earth's surface is enough to place GEO 4 unit SBIRS across 90 deg.

Today, the development using internet of things enables activities surrounding us to be monitored, controlled, predicted and calculated remotely through connections to the internet network such as monitoring activities of long-distance flood warning with information technology. Applying an information technology in the field of flood earlywarning has been developed in the world, either connected to internet network or not. The internet network that has been done in this paper is the design of WiFi network to access data of rainfall, water level and flood status at any time with a smartphone coming from flood earlywarning system. The results obtained when test of data accessing with smartphone are in form of rainfall and water level graphs against time and flood status indicators consisting of 3 flood states: Standby 2, Standby 1 and Flood. It is concluded that data are from flood earlywarning system has been able to accessed and displayed on smartphone via WiFi network in any time and real time.

The GAGE facility, managed by UNAVCO, maintains and operates about 1300 GNSS stations distributed across North and Central America as part of the EarthScope Plate Boundary Observatory (PBO) and the Continuously Operating Caribbean GPS Observational Network (COCONet). UNAVCO has upgraded about 450 stations in these networks to real-time and high-rate (RT-GNSS) and included surface meteorological instruments. The majority of these streaming stations are part of the PBO but also include approximately 50 RT-GNSS stations in the Caribbean and Central American region as part of the COCONet and TLALOCNet projects. Based on community input UNAVCO has been exploring ways to increase the capability and utility of these resources to improve our understanding in diverse areas of geophysics including seismic, volcanic, magmatic and tsunami deformation sources, extreme weather events such as hurricanes and storms, and space weather. The RT-GNSS networks also have the potential to profoundly transform our ability to rapidly characterize geophysical events, provide earlywarning, as well as improve hazard mitigation and response. Specific applications currently under development with university, commercial, non-profit and government collaboration on national and international scales include earthquake and tsunami earlywarning systems and near real-time tropospheric modeling of hurricanes and precipitable water vapor estimate assimilation. Using tsunami earlywarning as an example, an RT-GNSS network can provide multiple inputs in an operational system starting with rapid assessment of earthquake sources and associated deformation which informs the initial modeled tsunami. The networks can then can also provide direct measurements of the tsunami wave heights and propagation by tracking the associated ionospheric disturbance from several 100's of km away as the waves approaches the shoreline. These GNSS based constraints can refine the tsunami and inundation models and potentially

The Boxing Day Tsunami of 2004 caused an information catastrophy. Crucial earlywarning information could not be delivered to the communities under imminent threat, resulting in over 240,000 casualties in 14 countries. This tragedy sparked the development of a new generation of integrated modular Tsunami EarlyWarning Systems (TEWS). While significant advances were accomplished in the past years, recent events, like the Chile 2010 and the Tohoku 2011 tsunami demonstrate that the key technical challenge for Tsunami EarlyWarning research on the supranational scale still lies in the timely issuing of status information and reliable earlywarning messages. A key challenge stems from the main objective of the IOC Tsunami Programme, the integration of national TEWS towards ocean-wide networks: Each of the increasing number of integrated Tsunami EarlyWarning Centres has to cope with the continuing evolution of sensors, hardware and software while having to maintain reliable inter-center information exchange services. To avoid future information catastrophes, the performance of all components, ranging from sensors to Warning Centers, has to be regularly validated against defined criteria. This task is complicated by the fact that in term of ICT system life cycles tsunami are very rare event resulting in very difficult framing conditions to safeguard the availability and reliability of TWS. Since 2004, GFZ German Research Centre for Geosciences (GFZ) has built up expertise in the field of TEWS. Within GFZ, the Centre for GeoInformation Technology (CEGIT) has focused its work on the geoinformatics aspects of TEWS in two projects already: The German Indonesian Tsunami EarlyWarning System (GITEWS) funded by the German Federal Ministry of Education and Research (BMBF) and the Distant EarlyWarning System (DEWS), a European project funded under the sixth Framework Programme (FP6). These developments are continued in the TRIDEC project (Collaborative, Complex, and Critical

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, earlywarning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

Describes methods used in a project at George Washington University's Rehabilitation Research and Training Center to predict and classify a family's involvement in a patient's rehabilitation program. As family attitudes can enhance or damage a program's effectiveness, early identification of uncooperative families is necessary so that intervention…

As a global effort toward improving patient safety, a specific area of focus has been the early recognition and rapid intervention in deteriorating ward patients. This focus on "failure to rescue" has led to the construction of earlywarning/track-and-trigger systems. In this review article, we present a description of the data behind the creation and implementation of such systems, including multiple algorithms and strategies for deployment. Additionally, the strengths and weaknesses of the various systems and their evaluation in the literature are emphasized. Despite the limitations of the current literature, the potential benefit of these earlywarning/track-and-trigger systems to improve patient outcomes remains significant. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Famine earlywarning systems use remote sensing in combination with socio-economic and household food economy analysis to provide timely and rigorous information on emerging food security crises. The Famine EarlyWarning Systems Network (FEWS NET) is the US Agency for International Development's decision support system in 20 African countries, as well as in Guatemala, Haiti and Afghanistan. FEWS NET provides early and actionable policy guidance for the US Government and its humanitarian aid partners. As we move into an era of climate change where weather hazards will become more frequent and severe, understanding how to provide quantitative and actionable scientific information for policy makers using biophysical data is critical for an appropriate and effective response.

The EU-FP7 project EUPORIAS is developing a prototype climate service to enhance the existing food security drought earlywarning system in Ethiopia. The Livelihoods, Early Assessment and Protection (LEAP) system is the Government of Ethiopia's national food security earlywarning system, established with the support of WFP and the World Bank in 2008. LEAP was designed to increase the predictability and timeliness of response to drought-related food crises in Ethiopia. It combines earlywarning with contingency planning and contingency funding, to allow the government, WFP and other partners to provide early assistance in anticipation of an impending catastrophes. Currently, LEAP uses satellite based rainfall estimates to monitor drought conditions and to compute needs. The main aim of the prototype is to use seasonal hindcast data to assess the added value of using ensemble climate rainfall forecasts to estimate the cost of assistance of population hit by major droughts. We outline the decision making process that is informed by the prototype climate service, and we discuss the analysis of the expected and skill of the available rainfall forecast data over Ethiopia. One critical outcome of this analysis is the strong dependence of the expected skill on the observational estimate assumed as reference. A preliminary evaluation of the full prototype products (drought indices and needs estimated) using hindcasts data will also be presented.

Rainfall-induced landslide has been one of the major disasters in Korea since the beginning of 21st century when the global climate change started to give rise to the growth of the magnitude and frequency of extreme precipitation events. In order to mitigate the increasing damage to properties and loss of lives and to provide an effective tool for public officials to manage the landslide disasters, a real-time landslide earlywarning system with an advanced concept has been developed by taking into account for Busan, the second largest metropolitan city in Korea, as an operational test-bed. The system provides with warning information based on a five-level alert scheme (Normal, Attention, Watch, Alert, and Emergency) using the forecasted/observed rainfall data or the data obtained from ground monitoring (volumetric water content and matric suction). The alert levels are determined by applying seven different thresholds in a step-wise manner following a decision tree. In the pursuit of improved reliability of an earlywarning level assigned to a specific area, the system makes assessments repetitively using the thresholds of different theoretical backgrounds including statistical(empirical), physically-based, and mathematical analyses as well as direct measurement-based approaches. By mapping the distribution of the five earlywarning levels determined independently for each of tens of millions grids covering the entire mountainous area of Busan, the regional-scale system can also provide with the earlywarning information for a specific local area. The fact that the highest warning level is determined by using a concept of a numerically-modelled potential debris-flow risk is another distinctive feature of the system. This study tested the system performance by applying it for four previous rainy seasons in order to validate the operational applicability. During the rainy seasons of 2009, 2011, and 2014, the number of landslides recorded throughout Busan's territory

The Massachusetts Department of Elementary and Secondary Education (Department) created the grades 1-12 EarlyWarning Indicator System (EWIS) in response to district interest in the EarlyWarning Indicator Index (EWII) that the Department previously created for rising grade 9 students. Districts shared that the EWII data were helpful, but also…

A Smart Grid is a cyber-based tool to facilitate a network of sensors for monitoring and communicating the landslide hazard and providing the earlywarning. The sensor is designed as an electronic sensor installed in the existing monitoring and earlywarning instruments, and also as the human sensors which comprise selected committed-people at the local community, such as the local surveyor, local observer, member of the local task force for disaster risk reduction, and any person at the local community who has been registered to dedicate their commitments for sending reports related to the landslide symptoms observed at their living environment. This tool is designed to be capable to receive up to thousands of reports/information at the same time through the electronic sensors, text message (mobile phone), the on-line participatory web as well as various social media such as Twitter and Face book. The information that should be recorded/ reported by the sensors is related to the parameters of landslide symptoms, for example the progress of cracks occurrence, ground subsidence or ground deformation. Within 10 minutes, this tool will be able to automatically elaborate and analyse the reported symptoms to predict the landslide hazard and risk levels. The predicted level of hazard/ risk can be sent back to the network of electronic and human sensors as the earlywarning information. The key parameters indicating the symptoms of landslide hazard were recorded/ monitored by the electrical and the human sensors. Those parameters were identified based on the investigation on geological and geotechnical conditions, supported with the laboratory analysis. The cause and triggering mechanism of landslide in the study area was also analysed in order to define the critical condition to launch the earlywarning. However, not only the technical but also social system were developed to raise community awareness and commitments to serve the mission as the human sensors, which will

Drought prone states such as Texas requires creditable and actionable drought earlywarning ranging from seasonal to multi-decadal scales. Such information cannot be simply extracted from the available climate prediction and projections because of their large uncertainties at regional scales and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA national multi-models ensemble experiment (NMME) and the IPCC AR5 (CMIP5) models, are much more reliable for winter and spring than for the summer season for the US Southern Plains. They also show little connection between the droughts in winter/spring and those in summer, in contrast to the observed dry memory from spring to summer over that region. To mitigate the weakness of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies. Based on these key processes and related fields, we have developed a multivariate principle component statistical model to provide a probabilistic summer drought earlywarning indicator, using the observed or predicted climate conditions in winter and spring on seasonal scale and climate projection for the mid-21stcentury. The summer drought earlywarning indicator is constructed in a similar way to the NOAA probabilistic predictions that are familiar to water resource managers. The indicator skill is assessed using the standard NOAA climate prediction assessment tools, i.e., the two alternative forced choice (2AFC) and the Receiver Operating Characteristic (ROC). Comparison with long-term observations suggest that this summer drought earlywarning indicator is able to capture nearly all the strong summer droughts and outperform the dynamic prediction in this regard over the US Southern Plains. This earlywarning indicator has been used by the state water agency in May 2014 in briefing the state

Extreme rainfall is the main cause of shallow landslides. For risk mitigation, landslide earlywarning systems can be implemented, on the basis of rainfall monitoring and forecasting, and the use of a landslide triggering model. Several empirical, also referred to as statistical, rainfall-landslide triggering models have been proposed in the scientific literature, and used for earlywarning systems activated worldwide. Nonetheless, it is not clear how effective are landslide warning systems, and it is difficult to quantify the induced benefits for the implemented ones. Many rainfall thresholds have been determined through the statistical analysis of the rainfall events that have been the cause of past landslides only, thus neglecting the cases of true negatives and false positives, with negative effects on the robustness of the proposed threshold and, probably, on the effectiveness of the warning system. In the present work we address the issue of establishing warning thresholds, which, although in an approximate way, account for the related benefits. We propose the maximization of an objective function, that measures the trade-off between true and false warning issues. A ratio between the disadvantages of false positive and false negatives, not greater than one, is introduced in the function. The effect of this ratio on the determination of the thresholds is analysed. The proposed method is based on the availability of a continuous rainfall time series. In Italy, continuous rainfall time series are available from the 1920s, but practical difficulties arise for using them, as they are not published in the Hydrological Annual Reports, by the Servizio Idrografico e Mareografico Nazionale (National Hydrologic and Oceanographic Service), the manager of the most important rainfall monitoring network in Italy. However, it is possible to have a good approximation of the most intense rainfall events, in terms total rainfall, by using the data of annual maxima of

Scientific GNSS networks are moving towards a model of real-time data acquisition, epoch-by-epoch storage integrity, and on-board real-time position and displacement calculations. This new paradigm allows the integration of real-time, high-rate GNSS displacement information with acceleration and velocity data to create very high-rate displacement records. The mating of these two instruments allows the creation of a new, very high-rate (200 Hz) displacement observable that has the full-scale displacement characteristics of GNSS and high-precision dynamic motions of seismic technologies. It is envisioned that these new observables can be used for earthquakeearlywarning studies, volcano monitoring, and critical infrastructure monitoring applications. Our presentation will focus on the characteristics of GNSS, seismic, and strong motion sensors in high dynamic environments, including historic earthquakes replicated on a shake table over a range of displacements and frequencies. We will explore the optimum integration of these sensors from a filtering perspective including simple harmonic impulses over varying frequencies and amplitudes and under the dynamic conditions of various earthquake scenarios. We will also explore the tradeoffs between various GNSS processing schemes including real-time precise point positioning (PPP) and real-time kinematic (RTK) as applied to seismogeodesy. In addition we will discuss implementation of a Rapid Seismic Event Notification System that provides quick delivery of digital data from seismic stations to the acquisition and processing center and a full data integrity model for real-time earthquake notification that provides warning prior to significant ground shaking.

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe earlywarnings in Chinese construction projects. By combining the current state of earlywarning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety earlywarnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe earlywarning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe earlywarning applications, effective means and intelligent technology for a safe highway construction earlywarning system are established. PMID:24191134

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe earlywarnings in Chinese construction projects. By combining the current state of earlywarning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety earlywarnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe earlywarning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe earlywarning applications, effective means and intelligent technology for a safe highway construction earlywarning system are established.

With climate change, there has been an increase in the frequency, intensity and duration of heatwave events. In response to the devastating mortality and morbidity of recent heatwave events, many countries have introduced heatwave earlywarning systems (HEWS). HEWS are designed to reduce the avoidable human health consequences of heatwaves through timely notification of prevention measures to vulnerable populations. To identify the key characteristics of HEWS in European countries to help inform modification of current, and development of, new systems and plans. We searched the internet to identify HEWS policy or government documents for 33 European countries and requested information from relevant organizations. We translated the HEWS documents and extracted details on the trigger indicators, thresholds for action, notification strategies, message intermediaries, communication and dissemination strategies, prevention strategies recommended and specified target audiences. Twelve European countries have HEWS. Although there are many similarities among the HEWS, there also are differences in key characteristics that could inform improvements in heatwave earlywarning plans.

Empirical evidence for large-scale abrupt changes in ecosystems such as lakes and vegetation of semi-arid regions is growing. Such changes, called regime shifts, can lead to degradation of ecological services. We study simple ecological models that show a catastrophic transition as a control parameter is varied and propose a novel earlywarning signal that exploits two ubiquitous features of ecological systems: nonlinearity and large external fluctuations. Either reduced resilience or increased external fluctuations can tip ecosystems to an alternative stable state. It is shown that changes in asymmetry in the distribution of time series data, quantified by changing skewness, is a model-independent and reliable earlywarning signal for both routes to regime shifts. Furthermore, using model simulations that mimic field measurements and a simple analysis of real data from abrupt climate change in the Sahara, we study the feasibility of skewness calculations using data available from routine monitoring.

To be effective, earlywarning systems for natural hazards need to have not only a sound scientific and technical basis, but also a strong focus on the people exposed to risk, and with a systems approach that incorporates all of the relevant factors in that risk, whether arising from the natural hazards or social vulnerabilities, and from short-term or long-term processes. Disasters are increasing in number and severity and international institutional frameworks to reduce disasters are being strengthened under United Nations oversight. Since the Indian Ocean tsunami of 26 December 2004, there has been a surge of interest in developing earlywarning systems to cater to the needs of all countries and all hazards.

Famine earlywarning organizations have experience that has much to contribute to efforts to incorporate climate and weather information into economic and political systems. Food security crises are now caused almost exclusively by problems of food access, not absolute food availability, but the role of monitoring agricultural production both locally and globally remains central. The price of food important to the understanding of food security in any region, but it needs to be understood in the context of local production. Thus remote sensing is still at the center of much food security analysis, along with an examination of markets, trade and economic policies during food security analyses. Technology including satellite remote sensing, earth science models, databases of food production and yield, and modem telecommunication systems contributed to improved food production information. Here we present an econometric approach focused on bringing together satellite remote sensing and market analysis into food security assessment in the context of earlywarning.

Work of the EarlyWarning and Crop Condition Assessment (EW/CCA) project, one of eight projects in the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS), is reviewed. Its mission, to develop and test remote sensing techniques that enhance operational methodologies for crop condition assessment, was in response to initiatives issued by the Secretary of Agriculture. Meteorologically driven crop stress indicator models have been developed or modified for wheat, maize, grain sorghum, and soybeans. These models provide earlywarning alerts of potential or actual crop stresses due to water deficits, adverse temperatures, and water excess that could delay planting or harvesting operations. Recommendations are given for future research involving vegetative index numbers and the NOAA and Landsat satellites.

This study proposes a new simulation platform named Simulation Integrated Management (SIM) for the analysis of parallel and distributed systems. The platform eases the process of designing and testing both applications and architectures. The main characteristics of SIM are flexibility, scalability, and expandability. To improve the efficiency of project development, new models of early-warning satellite system were designed based on the SIM platform. Finally, through a series of experiments, the correctness of SIM platform and the aforementioned early-warning satellite models was validated, and the systematical analyses for the orbital determination precision of the ballistic missile during its entire flight process were presented, as well as the deviation of the launch/landing point. Furthermore, the causes of deviation and prevention methods will be fully explained. The simulation platform and the models will lay the foundations for further validations of autonomy technology in space attack-defense architecture research.

Based on social physics, this paper designs the index system of food safety, builds earlywarning model of food safety, calculates the degree of food safety, and assesses the state of earlywarning of 2007 in China. The result shows the degree of food safety is near 0.7 in securer state, belonging to slight emergency. It is much lower in eastern areas of developed regions, belonging to insecure state in the mass. That the food safety is ensured in major grain producing areas, Inner Mongolia, Ningxia and Xinjiang is the prerequisite of realizing the food safety of China. The result also shows four significant indices, grain production capacity, grain circulation order, grain demand and grain supply, which are important indicatio to control food safety.

Recently, a prototype dengue earlywarning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This earlywarning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

The present paper addresses the newly developed earlywarning technology that can help mitigate the slope failure disasters during heavy rains. Many studies have been carried out in the recent times on earlywarning that is based on rainfall records. Although those rainfall criteria of slope failure tells the probability of disaster on a regional scale, it is difficult for them to judge the risk of particular slopes. This is because the rainfall intensity is spatially too variable to forecast and the earlywarning based on rainfall alone cannot take into account the effects of local geology, hydrology and topography that vary spatially as well. In this regard, the authors developed an alternative technology in which the slope displacement/deformation is monitored and earlywarning is issued when a new criterion is satisfied. The new MEMS-based sensor monitors the tilting angle of an instrument that is embedded at a very shallow depth and the record of the tilting angle corresponds to the lateral displacement at the slope surface. Thus, the rate of tilting angle that exceeds a new criterion value implies an imminent slope failure. This technology has been validated against several events of slope failures as well as against a field rainfall test. Those validations have made it possible to determine the criterion value of the rate of tilting angle to be 0.1 degree/hour. The advantage of the MEMS tilting sensor lies in its low cost. Hence, it is possible to install many low-cost sensors over a suspected slope in which the precise range of what is going to fall down during the next rainfall is unknown. In addition to the past validations, this paper also introduces a recent application to a failed slope in the Izu Oshima Island where a heavy rainfall-induced slope failure occurred in October, 2013.

Earlywarning for rainfall-induced shallow landsliding can help reduce fatalities and economic losses. Although these commonly occurring landslides are typically triggered by subsurface hydrological processes, most earlywarning criteria rely exclusively on empirical rainfall thresholds and other indirect proxies for subsurface wetness. We explore the utility of explicitly accounting for antecedent wetness by integrating real-time subsurface hydrologic measurements into landslide earlywarning criteria. Our efforts build on previous progress with rainfall thresholds, monitoring, and numerical modeling along the landslide-prone railway corridor between Everett and Seattle, Washington, USA. We propose a modification to a previously established recent versus antecedent (RA) cumulative rainfall thresholds by replacing the antecedent 15-day rainfall component with an average saturation observed over the same timeframe. We calculate this antecedent saturation with real-time telemetered measurements from five volumetric water content probes installed in the shallow subsurface within a steep vegetated hillslope. Our hybrid rainfall versus saturation (RS) threshold still relies on the same recent 3-day rainfall component as the existing RA thresholds, to facilitate ready integration with quantitative precipitation forecasts. During the 2015–2017 monitoring period, this RS hybrid approach has an increase of true positives and a decrease of false positives and false negatives relative to the previous RA rainfall-only thresholds. We also demonstrate that alternative hybrid threshold formats could be even more accurate, which suggests that further development and testing during future landslide seasons is needed. The positive results confirm that accounting for antecedent wetness conditions with direct subsurface hydrologic measurements can improve thresholds for alert systems and earlywarning of rainfall-induced shallow landsliding.

Periodic epidemics of malaria are a major public health problem for many sub-Saharan African countries. Populations in epidemic prone areas have a poorly developed immunity to malaria and the disease remains life threatening to all age groups. The impact of epidemics could be minimized by prediction and improved prevention through timely vector control and deployment of appropriate drugs. Malaria EarlyWarning Systems are advocated as a means of improving the opportunity for preparedness and timely response.Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria EarlyWarning Systems for sub-Saharan Africa, as outlined by the World Health Organization.The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to earlywarning efforts. In response to these recommendations, the Famine EarlyWarning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHO's Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological

Drought is a hazard that lends itself well to diligent, sustained monitoring and earlywarning. However, unlike most hazards, the fact that droughts typically evolve slowly, can last for months or years and cover vast areas spanning multiple political boundaries/jurisdictions and economic sectors can make it a daunting task to monitor, develop plans for, and identify appropriate, proactive mitigation strategies. The National Drought Mitigation Center (NDMC) and National Integrated Drought Information System (NIDIS) have been working together to reduce societal vulnerability to drought by helping decision makers at all levels to: 1) implement drought earlywarning/forecasting and decision support systems; 2) support and advocate for better collection of, and understanding of drought impacts; and 3) increase long-term resilience to drought through proactive planning. The NDMC and NIDIS risk management approach has been the basis from which many partners around the world are developing a collaboration and coordination nexus with an ultimate goal of building comprehensive global drought earlywarning information systems (GDEWIS). The core emphasis of this model is on developing and applying useful and usable information that can be integrated and transferred freely to other regions around the globe. The High-Level Ministerial Declaration on Drought, the Integrated Drought Management Programme (IDMP) co-led by the WMO and the Global Water Partnership (GWP), and the Global Framework for Climate Services are drawing extensively from the integrated NDMC-NIDIS risk management framework. This presentation will describe, in detail, the various drought resources, tools, services, and collaborations already being provided and undertaken at the national and regional scales by the NDMC, NIDIS, and their partners. The presentation will be forward-looking, identifying improvements in existing and proposed mechanisms to help strengthen national and international drought early